User Comfort Research Articles

SMART HOME ENERGY SAVING WITH BIG DATA AND MACHINE LEARNING

In response to escalating energy consumption, particularly within the housing sector, a global imperative to reduce energy usage has emerged, propelling the concept of "smart houses" to the forefront of innovation. This paradigm shift owes its genesis to the convergence of advancements in energy conversion, communication networks, and information technology, catalyzing the emergence of the Internet of Things (IoT). The IoT facilitates seamless connectivity of devices via the World Wide Web, enabling remote management, monitoring, and detection capabilities. Capitalizing on this technological synergy, the integration of IoT, big data, and machine learning with home automation systems holds immense promise for enhancing energy efficiency. This paper introduces HEMS-IoT, a groundbreaking energy control system for intelligent homes, underpinned by big data analytics and machine learning algorithms, prioritizing security, convenience, and energy conservation. Leveraging J48 neural network technology and the Weka API, the study illuminates user behaviors and energy consumption patterns, enabling household classification based on energy usage profiles. Moreover, to ensure user comfort and safety, RuleML and Apache Mahout are deployed to customize energy-saving recommendations tailored to individual preferences. By presenting a practical demonstration of smart home monitoring, this paper validates the effectiveness of the proposed approach in enhancing security, comfort, and energy conservation. This pioneering research not only showcases the transformative potential of IoT-driven energy management systems but also sets the stage for a sustainable and interconnected future.

Knowledge-network-embedded deep reinforcement learning: An innovative way to high-efficiently develop an energy management strategy for the integrated energy system with renewable energy sources and multiple energy storage systems

To achieve efficient energy management in complex integrated energy systems (IESs) with renewable energy sources (RESs) and multiple energy storage systems (ESSs), the study aims to propose a novel approach. Evolutionary-based methods are difficult to find the optimal scheme, while deep reinforcement learning (DRL)-based methods face problems with low training efficiency. To address the limitations, a knowledge-network-embedded DRL method is proposed, which combines the fast-searching capability of genetic algorithm (GA) with the environment-aware decision-making capability of DRL, with the aim of developing a highly efficient energy management strategy in improving user comfort and reducing operating costs. GA is used to solve the IES optimization model for obtaining a solution set that serves as domain knowledge, and a knowledge network is constructed using deep learning (DL). Then, DRL uses this network as an actor network to explore the optimal strategy. The use of GA helps the DRL to enhance training efficiency and find an optimal strategy. Simulations show significant improvements in cost reduction (5.7 %) and user comfort (4.2 %), along with improved training efficiency (58.3 %) compared to baseline DRLs.

Application of energy scheduling algorithm based on energy consumption prediction in smart home energy scheduling

To effectively optimize household energy management, enhance user comfort, and ensure the stability of the power grid, this paper directs its focus towards household equipment and presents inter-household and indoor energy scheduling algorithms. Grounded in a two-dimensional dynamic device priority framework and predictive device operation modeling, this study demonstrates that the utilization of scheduling algorithms can avert grid overload scenarios during periods of low electricity prices. This results in more consistent hourly electricity consumption and a substantial peak-to-average ratio reduction of up to 44.86 %. Additionally, the application of the continuous wavelet neural network algorithm proves to be proficient in accurately forecasting home device operations. When the time was 17.5 h, the actual energy consumption of the water heater was 7.12 kWh, which was 0.78 kWh more than the predicted energy consumption. Utilizing scheduling algorithms, as opposed to not employing them, led to notable reductions in electricity expenses, demonstrating a cost savings rate of 15.32 % on the third day. Additionally, it facilitated decreased usage of the power grid. When the time was 18 o'clock, the power consumption of the grid using scheduling algorithms was 0.24 kWh, which was 0.25 kWh lower than that without scheduling algorithms. Following the implementation of the scheduling algorithm, minimal deviation in device startup times was observed during periods of high comfort, with offsets ranging from 0 h. The research methods employed demonstrate efficacy in orchestrating energy distribution among households and indoor spaces, thereby striking a harmonious balance between energy conservation and user comfort.

Influence of Thermal Comfort on Energy Consumption for Building Occupants: The Current State of the Art

Thermal comfort is a complex issue in the built environment due to the physiological and psychological differences of each individual in a building. There is a growing worry over the environmental implications of energy use as a result of the warming of the global climate and the growth in the number of instances of extreme weather events. Many review articles have been written, but these reviews have focused on a specific aspect of occupant behavior and thermal comfort. To research the trends of thermal comfort and energy, this research adopted mixed reviews, i.e., quantitative and qualitative, to understand the state-of-the-art factors affecting the thermal comfort of occupants concerning energy, different occupant modeling approaches, functions, and limitations. The in-depth qualitative discussion provides deeper insights into the impacts of occupant behaviors, factors affecting thermal comfort, and occupant behavior modeling approaches. This study classified occupant behaviors into five categories: occupant characteristics, perceptions of the occupant, realistic behaviors, heat gain, and occupant interactions with the system. It also went further to classify the factors affecting the thermal comfort of users based on past works of literature. These include structural, environmental, and human factors. It was concluded that factors that have the most significant impact on energy are human, structural, and environmental factors, respectively. In addition, most of the occupant behavior modeling approaches that have been used in past studies have pros and cons and cannot accurately predict human behaviors because they are stochastic. Future research should be conducted on thermal comfort for different building functions by examining the varied activity intensity levels of users, especially in educational or commercial buildings. Additionally, a proper investigation should be carried out on how thermal insulation of structural members influences thermal comfort. These should be compared in two similar buildings to understand occupant behavioral actions and energy consumption.

Ultrasensitive textile strain sensors redefine wearable silent speech interfaces with high machine learning efficiency

This work introduces a silent speech interface (SSI), proposing a few-layer graphene (FLG) strain sensing mechanism based on thorough cracks and AI-based self-adaptation capabilities that overcome the limitations of state-of-the-art technologies by simultaneously achieving high accuracy, high computational efficiency, and fast decoding speed while maintaining excellent user comfort. We demonstrate its application in a biocompatible textile-integrated ultrasensitive strain sensor embedded into a smart choker, which conforms to the user’s throat. Thanks to the structure of ordered through cracks in the graphene-coated textile, the proposed strain gauge achieves a gauge factor of 317 with <5% strain, corresponding to a 420% improvement over existing textile strain sensors fabricated by printing and coating technologies reported to date. Its high sensitivity allows it to capture subtle throat movements, simplifying signal processing and enabling the use of a computationally efficient neural network. The resulting neural network, based on a one-dimensional convolutional model, reduces computational load by 90% while maintaining a remarkable 95.25% accuracy in speech decoding. The synergy in sensor design and neural network optimization offers a promising solution for practical, wearable SSI systems, paving the way for seamless, natural silent communication in diverse settings.

Analyzing energy consumption due to occupant interaction with manual and automatic window blinds in multiple climates

Proper daylight management through automated or manual blind systems can enhance user comfort and energy conservation. Furthermore, the influence of weather conditions, coupled with occupant behavior, significantly impacts energy consumption, rendering weather forecasts invaluable in conducting individual building energy simulations to identify energy-efficient strategies for the future. Also, I wonder if it is possible to move some items from room 308 until you can provide items from other resources. Future weather conditions and their impact on building performance can be evaluated using Representative Concentration Pathways (RCP) scenarios provided by the Intergovernmental Panel on Climate Change (IPCC). This research aimed to compare the energy usage of manual and automated window blinds with internal blinds in mid-size office buildings across four main climate zones (2B Hot-Dry; 2A Hot-Humid; 7 Very Cold; 4A Mixed-Humid) in the USA under current and future climate change scenarios (RCP 4.5, 8.5). Utilizing simulation as the methodology and comparing the energy use resulting from the RCP 4.5 and 8.5 scenarios revealed that the most significant decrease in heating energy usage was happening in the Hot-Humid zone with Automated blinds, which is a more efficient alternative than manual Blinds. In contrast, the Very Cold zone experienced almost no changes in either heating or cooling energy consumption. In Climate Zones 2B Hot-Dry and 7 Very Cold, rising temperatures lead to increased cooling energy requirements and reduced heating demands due to climate changes. Climate Zone 4A Mixed-Humid saw reduced energy consumption for cooling but increased usage for heating, particularly under RCP 8.5. In Zone 2A Hot-Humid, a high demand for heating energy was found.

SOLAR BASED WHEEL CHAIR FOR DISABLED

The Solar Based Wheelchair for Disabled (SBWCD) is a groundbreaking project aimed at enhancing mobility and independence for individuals with disabilities by harnessing the power of solar energy. Traditional wheelchairs often rely on manual propulsion or battery-powered motors, which can be limiting in terms of range, accessibility to charging stations, and environmental impact. In response to these challenges, the SBWCD project introduces a novel approach by integrating solar panels into the design of wheelchairs to harness solar energy and convert it into electrical power for propulsion. This innovative solution offers numerous benefits, including extended range, reduced reliance on grid electricity, and decreased carbon footprint. The SBWCD features a lightweight and durable frame equipped with high-efficiency solar panels mounted on the backrest and armrests, maximizing exposure to sunlight while maintaining user comfort and ergonomics. The solar panels are connected to an onboard battery system, which stores excess energy generated during daylight hours for use during periods of low sunlight or at night. This hybrid power system ensures continuous operation of the wheelchair regardless of environmental conditions, providing users with greater autonomy and freedom of movement. Moreover, the SBWCD is designed with user-centric features and accessibility in mind. Adjustable seating, armrests, and footrests accommodate users of varying sizes and preferences, while ergonomic controls and intuitive interfaces ensure ease of operation for individuals with limited mobility or dexterity. Additionally, safety features such as anti-tip mechanisms, seat belts, and obstacle detection systems enhance user confidence and mitigate risks associated with wheelchair use in diverse environments. In summary, the Solar Based Wheelchair for Disabled (SBWCD) project represents a significant advancement in assistive technology, offering a sustainable and user-friendly solution for individuals with disabilities. By harnessing the abundant and renewable energy of the sun, SBWCD empowers users to navigate their surroundings with greater independence, efficiency, and environmental consciousness. As the demand for accessible and eco-friendly mobility solutions continues to grow, the SBWCD project serves as a beacon of innovation and inclusivity in the field of assistive technology.

AI-Enabled Smart Jacket : Revolutionizing Healthcare Monitoring and Environmental Control

The AI smart jacket represents a groundbreaking innovation in healthcare monitoring, merging wearable technology, advanced sensors, and machine learning algorithms. This wearable device integrates GPS and biometric sensors for real-time data capture, facilitating remote access to health metrics via wireless networking. With a focus on user comfort and privacy, the jacket features machine-washable fabrics and robust security measures. Its array of functionalities includes temperature adjustment, SOS capability, oximeter, ECG monitoring, pulse rate tracking, and a fan with massage functions. The jacket's versatility extends from chronic illness management to elderly care, promising to enhance overall well-being and quality of life. By seamlessly integrating technology into daily life, the AI smart jacket stands as a pioneering solution at the forefront of healthcare innovation.

Analyzing the Thermal Characteristics of Three Lining Materials for Plantar Orthotics.

The choice of materials for covering plantar orthoses or wearable insoles is often based on their hardness, breathability, and moisture absorption capacity, although more due to professional preference than clear scientific criteria. An analysis of the thermal response to the use of these materials would provide information about their behavior; hence, the objective of this study was to assess the temperature of three lining materials with different characteristics. The temperature of three materials for covering plantar orthoses was analyzed in a sample of 36 subjects (15 men and 21 women, aged 24.6 ± 8.2 years, mass 67.1 ± 13.6 kg, and height 1.7 ± 0.09 m). Temperature was measured before and after 3 h of use in clinical activities, using a polyethylene foam copolymer (PE), ethylene vinyl acetate (EVA), and PE-EVA copolymer foam insole with the use of a FLIR E60BX thermal camera. In the PE copolymer (material 1), temperature increases between 1.07 and 1.85 °C were found after activity, with these differences being statistically significant in all regions of interest (p < 0.001), except for the first toe (0.36 °C, p = 0.170). In the EVA foam (material 2) and the expansive foam of the PE-EVA copolymer (material 3), the temperatures were also significantly higher in all analyzed areas (p < 0.001), ranging between 1.49 and 2.73 °C for EVA and 0.58 and 2.16 °C for PE-EVA. The PE copolymer experienced lower overall overheating, and the area of the fifth metatarsal head underwent the greatest temperature increase, regardless of the material analyzed. PE foam lining materials, with lower density or an open-cell structure, would be preferred for controlling temperature rise in the lining/footbed interface and providing better thermal comfort for users. The area of the first toe was found to be the least overheated, while the fifth metatarsal head increased the most in temperature. This should be considered in the design of new wearables to avoid excessive temperatures due to the lining materials.

Optimizing the Use of Natural Lighting

Lighting constitutes a crucial element in spatial design aimed at enhancing user comfort. However, the efficacy of natural lighting in buildings remains suboptimal due to prevalent preferences for artificial illumination. This study identifies instances where the utilization of natural lighting, particularly sunlight, can be optimized, focusing on the Dean building of the Faculty of Engineering Unsrat, specifically the Library room, also known as the E-Library room. The objective is to investigate the most effective timing for natural lighting usage compared to artificial lighting from lamps.

Hakikat Belajar dan Pembelajaran

Learning is an activity that is deliberately carried out so that there is a change in one's abilities, by learning children who previously did not know, were unskilled, become knowledgeable and skilled. Meanwhile, learning is a process of interaction between educators and students, and learning resources in educational environmental units. Learning is assistance provided by educators so that knowledge, knowledge and mastery can be acquired. The use of various learning resources in educational institutions is always influenced by various factors, namely internal factors that have a dominant influence on the learning and learning process such as awareness, enthusiasm, attitudes, interests, metacognition, abilities, skills and personal comfort for users; Meanwhile, external factors are those that influence the availability of varied learning resources, ease of access to learning resources, learning processes, space, human resources, as well as traditions and systems currently in force in schools/educational institutions.

DEFORMATION OF END CROSSBEAMS OF STEEL BRIDGES RELATED TO SUSTAINABILITY OF BRIDGE JOINTS

Both AASHTO and Eurocode stipulate that the deflection of end crossbeams of bridges must be limited to 5 mm under the influence of frequent traffic loads. After a thorough literature review and survey of data from manufacturers of bridge joints, it must be concluded that the background of this requirement is not really based on facts. Durability of bridge joints certainly does not appear to be the basis. Another valid reason can be the comfort of road user, which is effectively disturbed by driving over bumps and thresholds. Although the road user expects light shocks at the end of a bridge, the acceleration inside the vehicle must be limited. Therefore, the state of a wheel driving over a threshold has been studied, by applying analytical equations taken from literature and extended. These allow a fairly accurate simulation of the state. Force required to cross the threshold produces an acceleration of wheel, which is transmitted to vehicle's sprung chassis. In the latter one finds the acceleration to which persons are subjected. Several values of comfort can be considered. It turns out that 5 mm limit is very conservative, and one could easily allow up to double the end cross beam’s deflection.

DEFORMATION OF END CROSSBEAMS OF STEEL BRIDGES RELATED TO SUSTAINABILITY OF BRIDGE JOINTS

Both AASHTO and Eurocode stipulate that the deflection of end crossbeams of bridges must be limited to 5 mm under the influence of frequent traffic loads. After a thorough literature review and survey of data from manufacturers of bridge joints, it must be concluded that the background of this requirement is not really based on facts. Durability of bridge joints certainly does not appear to be the basis. Another valid reason can be the comfort of road user, which is effectively disturbed by driving over bumps and thresholds. Although the road user expects light shocks at the end of a bridge, the acceleration inside the vehicle must be limited. Therefore, the state of a wheel driving over a threshold has been studied, by applying analytical equations taken from literature and extended. These allow a fairly accurate simulation of the state. Force required to cross the threshold produces an acceleration of wheel, which is transmitted to vehicle's sprung chassis. In the latter one finds the acceleration to which persons are subjected. Several values of comfort can be considered. It turns out that 5 mm limit is very conservative, and one could easily allow up to double the end cross beam’s deflection.

Effects of In-Wheel Suspension on Whole-Body Vibration and Comfort in Manual Wheelchair Users

Frequent and prolonged exposure to high levels of vibration and shock can cause neck and back pain and discomfort for many wheelchair users. Current methods to attenuate the vibration have shown to be ineffective and, in some cases, detrimental to health. Novel in-wheel suspension systems claim to offer a solution by replacing traditional spokes of the rear wheels with dampening elements or springs. The objective of this study was to investigate the effects of in-wheel suspension on reducing vibration and shock and improving comfort in manual wheelchair users. Twenty-four manual wheelchair users were propelled over nine different surfaces using a standard spoked wheel, a Spinergy CLX, and Loopwheels while accelerometry data was collected at the footrest, seat, and backrest. Loopwheels lowered vibrations by 10% at the backrest compared to the standard and CLX wheels (p-value &lt; 0.001) and by 7% at the footrest compared to the CLX (p-value &lt; 0.05). They also reduced shocks by 7% at the backrest compared to the standard wheel and CLX (p-value &lt; 0.001). No significant differences were found in comfort between the wheels. Results indicate that Loopwheels is effective at reducing vibration and shock, but more long-term testing is required to determine effects on health.

An empirical study on technology readiness level of industries to use green hydrogen in India: role of policy interventions

Purpose The purpose of this study is to explore the determinants influencing industrial adoption of green hydrogen amidst the global transition towards sustainability. Recognizing green hydrogen as a pivotal clean energy alternative for industrial applications is critical for understanding its potential integration into sustainable practices. Design/methodology/approach This research examines the impact of factors such as innovativeness, perceived ease of use, user comfort, optimism and governmental policies on the industrial intention towards green hydrogen usage. Using responses from 227 Indian industry professionals and conducting analysis via the SmartPLS software, the study reveals a discernible discomfort among industrial workers pertaining to the daily application of green hydrogen. Findings The research presents an array of policy recommendations for stakeholders. Emphasized strategies include the introduction of green hydrogen certificates, sustainable public procurement mechanisms, tax incentives, green labelling protocols and the establishment of a dedicated hydrogen skill development council, all of which can significantly influence the trajectory of green hydrogen adoption within the industrial sector. Originality/value This research synthesizes various elements, from industry perception and challenges to policy implications, presenting a holistic view of green hydrogen’s potential role in industry decarbonization and SDG realization. In essence, this study deepens not only the empirical understanding but also pioneers fresh theoretical frameworks, setting a precedent for subsequent academic endeavours.

Intelligent Lighting Control Systems for Energy Savings in Hospital Buildings Using Artificial Neural Networks

Lighting control systems are essential in modern building automation and smart homes, efficiently managing illumination to enhance energy conservation and user comfort. This project tackles energy consumption challenges in hospital buildings by introducing Intelligent Lighting Control Systems (ILCS) that take natural light and occupancy into account, driven by Artificial Neural Networks (ANN) and diverse machine learning algorithms. In our study, we collected sensor data, processed it, and designed a lighting control system employing a feedforward neural network and various machine learning algorithms. Surprisingly, our research found that a linear regression algorithm surpassed the ANN-based system in this context. We implemented a prototype, tested it on hardware, and obtained the expected results. This research marks progress towards optimizing energy use in hospital buildings and contributing to sustainability endeavors. By combining ILCS and machine learning, it offers a promising approach for more efficient and eco-friendly lighting systems

Recent Advances in Biomimetics for the Development of Bio-Inspired Prosthetic Limbs.

Recent advancements in biomimetics have spurred significant innovations in prosthetic limb development by leveraging the intricate designs and mechanisms found in nature. Biomimetics, also known as "nature-inspired engineering", involves studying and emulating biological systems to address complex human challenges. This comprehensive review provides insights into the latest trends in biomimetic prosthetics, focusing on leveraging knowledge from natural biomechanics, sensory feedback mechanisms, and control systems to closely mimic biological appendages. Highlighted breakthroughs include the integration of cutting-edge materials and manufacturing techniques such as 3D printing, facilitating seamless anatomical integration of prosthetic limbs. Additionally, the incorporation of neural interfaces and sensory feedback systems enhances control and movement, while technologies like 3D scanning enable personalized customization, optimizing comfort and functionality for individual users. Ongoing research efforts in biomimetics hold promise for further advancements, offering enhanced mobility and integration for individuals with limb loss or impairment. This review illuminates the dynamic landscape of biomimetic prosthetic technology, emphasizing its transformative potential in rehabilitation and assistive technologies. It envisions a future where prosthetic solutions seamlessly integrate with the human body, augmenting both mobility and quality of life.

Automated Water Meter Reading through Image Recognition

Abstract: In this paper, we propose a solution for reading an analog water meter automatically by digitalizing the meter. Since digital water meters are expensive, we retrofit the meter using Raspberry Pi, a camera, and an LED. Though the structure of the analog water meter is simple, consumes less power, and has high durability, it has some disadvantages. Primarily, it requires human effort for reading, which is time-consuming and causes inconvenience to users. To address this challenge, there is a need for an automated system. Thus, the project is implemented using image recognition techniques, specifically Optical Character Recognition (OCR), and Internet of Things (IoT). This method accurately detects the numerical values present on the meter, and this data is transmitted to ThingSpeak, an IoT platform. Overall, this project offers an effective solution for reading water meters and enhance the comfort and convenience of users.

Latent Heat Loss Through Fabrics During an Alternate Simulated Work–Rest Sequence

Humans produce different rates of sweating depending on the intensity level of a given activity. The clothing worn during the activity has a significant effect on the latent heat loss that can occur before, during, and after the activity. The water adsorption and spreading properties of a material yield differences in the amount of heat exchange that can occur between the person and the environment. Current test methods evaluate this using a value known as evaporative resistance, which is used to determine the heat exchange potential of a material. However, this value is only taken once the material has reached “steady state” and does not consider the sweating period before steady state is reached, or the drying period after steady state is reached. Therefore, an area under the curve (AUC) value was derived during these periods to compare the heat exchange properties of different materials. A sweating guarded hot plate was used to simulate different sweat rates, and therefore activity levels, to compare materials constructed of cotton, viscose, polyester, and wool. The overall latent heat loss of the hydrophobic wool was much less compared to the other samples with a gentler slope and lower AUC values than the other samples. It was found that the segment of time analyzed has a significant impact on the conclusion drawn about the latent heat loss of a material, p &lt; 0.0001. Sweat rate also has a significant impact on the heat loss value, p &lt; 0.0001, but is perhaps less useful when comparing samples to each other. It was found that comparing the different periods of the test could yield different conclusions when comparing materials for latent heat loss. Several current test methods do not consider phases outside of the steady state period and therefore miss valuable information pertinent to user comfort during these phases, especially in the post-exercise phase.

Fall Detection System based on iOS Smartphone Sensors

Activities of daily living (ADLs) are a crucial aspect of human life, especially in remote health monitoring and fall detection. As smartphones have become an integral part of our daily routines, with their ability to perform complex calculations, connect to the internet, and incorporate various sensors, researchers have been inspired to explore human activity recognition systems. This paper focuses on accelerometer and gyroscope data from iOS-based smartphones. We developed a data collection app to record fall types (e.g., Falling Right, Falling Left) and fall-like activities (e.g., Sitting Fast, Jumping). Volunteers carried smartphones naturally in their pockets during experiments, presenting a challenge of noise but enhancing user comfort. We applied different Machine Learning algorithms (Decision Trees, Random Forest, Logistic Regression, k-Nearest Neighbor, XGBoost, LightGBM, and Neural Networks) to analyze the collected dataset. In contrast to typical studies, our approach replicated real-world smartphone usage. The paper presents and analyzes promising results from the study. Furthermore, we implemented the trained model as a real-time mobile application for potential users. This research illustrates the potential of smartphones in fall detection and opens the way for user-friendly solutions in remote health monitoring.