Search Results (92)

In the era of homogenized competition, brand knowledge has become a critical factor that influences consumer purchasing decisions. However, traditional single-layer network models fail to capture the multi-dimensional semantic relationships embedded in brand-related textual data. To address this gap, this study proposes a BKMN framework integrating TF-IDF and TextRank algorithms for comprehensive brand knowledge discovery. By analyzing 19,875 consumer reviews of a mobile phone brand from JD website, we constructed a tri-layer network comprising TF-IDF-derived keywords, TextRank-derived keywords, and their overlapping nodes. The model incorporates co-occurrence matrices and centrality metrics (degree, closeness, betweenness, eigenvector) to identify semantic hubs and interlayer associations. The results reveal that consumers prioritize attributes such as “camera performance”, “operational speed”, “screen quality”, and “battery life”. Notably, the overlap layer exhibits the highest node centrality, indicating convergent consumer focus across algorithms. The network demonstrates small-world characteristics (average path length = 1.627) with strong clustering (average clustering coefficient = 0.848), reflecting cohesive consumer discourse around key features. Meanwhile, this study proposes the Mul-LSTM model for sentiment analysis of reviews, achieving a 93% sentiment classification accuracy, revealing that consumers have a higher proportion of positive attitudes towards the brand’s cell phones, which provides a quantitative basis for enterprises to understand users’ emotional tendencies and optimize brand word-of-mouth management. This research advances brand knowledge modeling by synergizing heterogeneous algorithms and multilayer network analysis. Its practical implications include enabling enterprises to pinpoint competitive differentiators and optimize marketing strategies. Future work could extend the framework to incorporate sentiment dynamics and cross-domain applications in smart home or cosmetic industries. Full article

This paper presents SEAMS (Solar Energy Aggregator Management System), an optimization-based framework for managing solar energy trading in smart communities under Thailand’s regulatory constraints. A major challenge is the prohibition of residential grid feed-in, which limits the use of conventional peer-to-peer energy models. Additionally, fixed pricing is required to ensure simplicity and trust among users. SEAMS coordinates prosumer and consumer households, a shared battery energy storage system (BESS), and a centralized aggregator (AGG) to minimize total electricity costs while maintaining financial neutrality for the aggregator. A mixed-integer linear programming (MILP) model is developed to jointly optimize PV sizing, BESS capacity, and internal buying price, accounting for Time-of-Use (TOU) tariffs and local policy limitations. Simulation results show that a 6 kW PV system and a 70–75 kWh shared BESS offer optimal performance. A 60:40 prosumer-to-consumer ratio yields the lowest total cost, with up to 49 percent savings compared to grid-only systems. SEAMS demonstrates a scalable and policy-aligned approach to support Thailand’s transition toward decentralized solar energy adoption and improved energy affordability. Full article

This paper introduces a high-efficiency buck converter designed for a wide load range, targeting low-power applications in medical devices, smart homes, wearables, IoT, and technology utilizing WiFi and Bluetooth. To achieve high efficiency across varying loads, the proposed converter employs a low-power adaptive on-time (AOT) controller that ensures output voltage stability and seamless mode transitions. An adaptive comparator (ACP) with variable output impedance is introduced, offering a variable DC gain and bandwidth to be suitable for different load conditions. A negative-level shifter (NLS) circuit, with its swing ranging from −0.5 V to the battery voltage (V_BAT), is proposed to control the smaller power p-MOS transistors. By using an NLS, the chip area, which is mostly occupied by power CMOS transistors, is reduced while the power efficiency is improved, particularly under a heavy load. A status time detector (STD) block which provides control signals to the ACP and NLS for optimized power consumption is added to identify load conditions (heavy, light, ultra-light). By employing a 180 nm CMOS technology, the active chip area occupies about 0.31 mm². With an input voltage range of 2.8–3.3 V, the controller’s current consumption ranges from 1.2 μA to 16 μA, corresponding to the output load current varying from 12 μA to 120 mA. Although the output load can vary, the output voltage is regulated at 1.2 V with a ripple between 3 and 12 mV. The proposed design achieves a peak efficiency of 96.2% under a heavy load with a switching frequency of 1.3 MHz. Full article

In smart homes, heavy reliance on appliance automation has increased, along with the energy demand in developing urban areas, making efficient energy management an important factor. To address the scheduling of appliances under Demand-Side Management, this article explores the use of heuristic-based optimization techniques (HOTs) in smart homes (SHs) equipped with renewable and sustainable energy resources (RSERs) and energy storage systems (ESSs). The optimal model for minimization of the peak-to-average ratio (PAR), considering user comfort constraints, is validated by using different techniques, such as the Genetic Algorithm (GA), Binary Particle Swarm Optimization (BPSO), Wind-Driven Optimization (WDO), Bacterial Foraging Optimization (BFO) and the Genetic Modified Particle Swarm Optimization (GmPSO) algorithm, to minimize electricity costs, the PAR, carbon emissions and delay discomfort. This research investigates the energy optimization results of three real-world scenarios. The three scenarios demonstrate the benefits of gradually assembling RSERs and ESSs and integrating them into SHs employing HOTs. The simulation results show substantial outcomes, as in the scenario of Condition 1, GmPSO decreased carbon emissions from 300 kg to 69.23 kg, reducing emissions by 76.9%; bill prices were also cut from an unplanned value of 400.00 cents to 150 cents, a 62.5% reduction. The PAR was decreased from an unscheduled value of 4.5 to 2.2 with the GmPSO algorithm, which reduced the value by 51.1%. The scenario of Condition 2 showed that GmPSO reduced the PAR from 0.5 (unscheduled) to 0.2, a 60% reduction; the costs were reduced from 500.00 cents to 200.00 cents, a 60% reduction; and carbon emissions were reduced from 250.00 kg to 150 kg, a 60% reduction by GmPSO. In the scenario of Condition 3, where batteries and RSERs were integrated, the GmPSO algorithm reduced the carbon emission value to 158.3 kg from an unscheduled value of 208.3 kg, a reduction of 24%. The energy cost was decreased from an unplanned value of 500 cents to 300 cents with GmPSO, decreasing the overall cost by 40%. The GmPSO algorithm achieved a 57.1% reduction in the PAR value from an unscheduled value of 2.8 to 1.2. Full article

The increasing adoption of intermittent renewable energy sources and electric vehicles in households necessitates effective energy management systems (EMS) in the residential sector. This study quantifies the economic benefits of using a state-of-the-art EMS for optimally controlling a grid-connected smart home, which includes PV panels, a battery, and an EV charging station with either monodirectional or bidirectional charging modes. The EMS uses a two-layer approach: the first layer handles strategic decisions with day-ahead forecasts and solving a mixed-integer linear program (MILP) model; the second layer manages the real-time control decisions based on a heuristic strategy. Tested on 396 real-world case studies (based on measured data) with varying user types and energy systems (different PV plant sizes, with or without BESS, and different EV charging modes), different EV models, and weekly commutes, the results demonstrate the EMS’s cost-effectiveness compared to current non-predictive heuristic strategies. Annual cost savings exceed 20% in all cases and reach up to 900 €/year for configurations with large (6 kW) PV plants. Additionally, while installing a battery is not economically advantageous, bidirectional EV chargers yield 10–15% additional savings compared to monodirectional chargers, increasing with more weekly remote working days. Full article

Governments are focusing on citizen participation in the energy transition, e.g., with dynamic electricity tariffs, which pass part of the wholesale price volatility to end users. While often the cheapest alternative, these tariffs require micromanagement for optimization. In this research, an automated system capable of supplying electricity for home use at minimal cost called Smart Relays and Controller (SRC) is presented. SRC scrapes prices online, charges a battery system during the cheapest time slots and supplies electricity to the home energy system from the cheapest source, either the battery or the grid, while optimizing battery life. To validate the system, a comparison is made between SRC, a programmable scheduler and PVPC (Spain’s dynamic tariff) using twenty-eight months of hourly historical data. SRC is shown to be superior to both the scheduler and PVPC, with the scheduler performing worse than SRC but better than PVPC (T.T., p < 0.001). SRC achieves a 36.16% discount over PVPC, 13.89% when factoring in battery life. The savings are 44.24% higher with SRC than with a scheduler. Neither inflation nor incentives to reduce costs are considered. While we studied Spain’s tariff, SRC would work in any country offering dynamic electricity tariffs, with benefit margins dependent on their particularities. Full article

There are over 300 million smart homes worldwide and 60.4 million smart homes in the US, using devices like smart thermostats, smart plugs, smart door locks, etc. Yet in this age of smart and connected devices, we still use paper-based sticky notes on doors to display messages such as “Busy, do not disturb”, “In a Zoom meeting”, etc. In this project, a novel IoT-connected digital sticky note system was developed where the user can wirelessly send messages from a smartphone to a sticky note display. The sticky note displays can be hung on the doors of offices, hotels, homes, etc. The display could be updated with the user’s message sent from anywhere in the world. The key design challenge was to develop the display unit to consume as little power as possible to increase battery life. A prototype of the proposed system was developed comprising ultra-low-power sticky note display units consuming only 404 µA average current and having a battery life of more than six months, with a Wi-Fi-connected hub unit, an MQTT server, and a smartphone app for composing the message. Full article

Smart microgrids (SMGs) have emerged as a key solution to enhance energy management and sustainability within decentralized energy systems. This paper presents SmartGrid AI, a platform integrating deep reinforcement learning (DRL) and neural networks to optimize energy consumption, predict demand, and facilitate peer-to-peer (P2P) energy trading. The platform dynamically adapts to real-time energy demand and supply fluctuations, achieving a 23% reduction in energy costs, a 40% decrease in grid dependency, and an 85% renewable energy utilization rate. Furthermore, AI-driven P2P trading mechanisms demonstrate that 18% of electricity consumption is handled through efficient decentralized exchanges. The integration of vehicle-to-home (V2H) technology allows electric vehicle (EV) batteries to store surplus renewable energy and supply 15% of household energy demand during peak hours. Real-time data from Saudi Arabia validated the system’s performance, highlighting its scalability and adaptability to diverse energy market conditions. The quantitative results suggest that SmartGrid AI is a revolutionary method of sustainable and cost-effective energy management in SMGs. Full article

Smart power grids (SGs) enhance efficiency, reliability, and sustainability by integrating distributed energy resources (DERs) such as solar panels and wind turbines. A key challenge in SGs is managing home battery charging during periods of insufficient renewable energy generation to ensure fairness, efficiency, and customer satisfaction. This paper introduces a secure reinforcement learning (RL)-based framework for optimizing battery charging coordination while addressing privacy concerns and false data injection (FDI) attacks. Privacy is preserved through Federated Learning (FL), enabling collaborative model training without sharing sensitive State of Charge (SoC) data that could reveal personal routines. To combat FDI attacks, Deep Learning (DL)-based detectors are deployed to identify malicious SoC data manipulation. To improve FL efficiency, the Change and Transmit (CAT) technique reduces communication overhead by transmitting only model parameters that experience enough change comparing to the last round. Extensive experiments validate the framework’s efficacy. The RL-based charging coordinator ensures fairness by maintaining SoC levels within thresholds and reduces overall power utilization through optimal grid power allocation. The CAT-FL approach achieves up to 93.5% communication overhead reduction, while DL-based detectors maintain high accuracy, with supervised models reaching 99.84% and anomaly detection models achieving 92.1%. Moreover, the RL agent trained via FL demonstrates strong generalization, achieving high cumulative rewards and equitable power allocation when applied to new data owners which did not participate in FL training. This framework provides a scalable, privacy-preserving, and efficient solution for energy management in SGs, offering high accuracy against FDI attacks and paving the way for the future of smart grid deployments. Full article

Smart cities embody a transformative approach to modernizing urban infrastructure and harness the power of deep learning (DL) and Vehicle-to-Home (V2H) technology to redefine home energy management. Neural network-based Q-learning algorithms optimize the scheduling of household appliances and the management of energy storage systems, including batteries, to maximize energy efficiency. Data preprocessing techniques, such as normalization, standardization, and missing value imputation, are applied to ensure that the data used for decision making are accurate and reliable. V2H technology allows for efficient energy exchange between electric vehicles (EVs) and homes, enabling EVs to act as both energy storage and supply sources, thus improving overall energy consumption and reducing reliance on the grid. Real-time data from photovoltaic (PV) systems are integrated, providing valuable inputs that further refine energy management decisions and align them with current solar energy availability. The system also incorporates battery storage (BS), which is critical in optimizing energy usage during peak demand periods and providing backup power during grid outages, enhancing energy reliability and sustainability. By utilizing data from a Tunisian weather database, smart cities significantly reduce electricity costs compared to traditional energy management methods, such as Dynamic Programming (DP), Rule-Based Systems, and Genetic Algorithms. The system’s performance is validated through robust AI models, performance metrics, and simulation scenarios, which test the system’s effectiveness under various energy demand patterns and changing weather conditions. These simulations demonstrate the system’s ability to adapt to different operational environments. Full article

Citrus farming in South Africa has become extremely lopsided in terms of economic opportunities. The statistics show that the wealthy large-scale farmers simultaneously control 100% of the international export market and 77.1% of the local market, hence endangering the prospect of the small- and medium-scale farmers. This research presents a novel, low-cost autonomous mobile robot (AMR) designed to support small- and medium-scale citrus farmers in South Africa, enhancing their competitiveness in both local and international markets. Developed using GENESYS software 2023 University Edition for systems integration, the AMR offers real-time crop monitoring to aid phytosanitary regulations compliance, autonomous navigation with object avoidance, error alerts, GPS functionality, and auto-homing when battery levels drop to 30%. Additionally, it captures periodic snapshots of citrus crops for visual inspection and assists with proof of protocols for sustaining citrus and treating infected trees, hence increasing its credibility and accountability for export and local markets. The AMR represents a significant advancement in affordable smart technology for sustainable citrus farming. Full article

Reinforcement learning (RL) is proven effective in optimizing home battery charging coordination within smart grids. However, its vulnerability to adversarial behavior poses a significant challenge to the security and fairness of the charging process. In this study, we, first, craft five stealthy false data injection (FDI) attacks that under-report the state-of-charge (SoC) values to deceive the RL agent into prioritizing their charging requests, and then, we investigate the impact of these attacks on the charging coordination system. Our evaluations demonstrate that attackers can increase their chances of charging compared to honest consumers. As a result, honest consumers experience reduced charging levels for their batteries, leading to a degradation in the system’s performance in terms of fairness, consumer satisfaction, and overall reward. These negative effects become more severe as the amount of power allocated for charging decreases and as the number of attackers in the system increases. Since the total available power for charging is limited, some honest consumers with genuinely low SoC values are not selected, creating a significant disparity in battery charging levels between honest and malicious consumers. To counter this serious threat, we develop a deep learning-based FDI attack detector and evaluated it using a real-world dataset. Our experiments show that our detector can identify malicious consumers with high accuracy and low false alarm rates, effectively protecting the RL-based charging coordination system from FDI attacks and mitigating the negative impacts of these attacks. Full article

Background/Objectives: The growing incidence of cognitive impairment among older adults has a significant impact on individuals, family members, caregivers, and society. Current conventional cognitive assessment tools are faced with some limitations. Recent evidence suggests that automating cognitive assessment holds promise, potentially resulting in earlier diagnosis, timely intervention, improved patient outcomes, and higher chances of response to treatment. Despite the advantages of automated assessment and technological advancements, automated cognitive assessment has yet to gain widespread use, especially in low and lower middle-income countries. This review highlights the potential of automated cognitive assessment tools and presents an overview of existing tools. Methods: This review includes 87 studies carried out with non-neuroimaging data alongside their performance metrics. Results: The identified articles automated the cognitive assessment process and were grouped into five categories either based on the tools’ design or the data analysis approach. These categories include game-based, digital versions of conventional tools, original computerized tests and batteries, virtual reality/wearable sensors/smart home technologies, and artificial intelligence-based (AI-based) tools. These categories are further explained, and evaluation of their strengths and limitations is discussed to strengthen their adoption in clinical practice. Conclusions: The comparative metrics of both conventional and automated approaches of assessment suggest that the automated approach is a strong alternative to the conventional approach. Additionally, the results of the review show that the use of automated assessment tools is more prominent in countries ranked as high-income and upper middle-income countries. This trend merits further social and economic studies to understand the impact of this global reality. Full article

This paper presents an Optimal Power Management System (OPMS) for smart homes in 6G environments, which are designed to enhance the sustainability of Green Internet of Everything (GIoT) applications. The system employs a brute-force search using an exact solution to identify the optimal decision for adapting power consumption to renewable power availability. Key techniques, including priority-based allocation, time-shifting, quality degradation, battery utilization and service rejection, will be adopted. Given the NP-hard nature of this problem, the brute-force approach is feasible for smaller scenarios but sets the stage for future heuristic methods in large-scale applications like smart cities. The OPMS, deployed on Multi-Access Edge Computing (MEC) nodes, integrates a novel demand response (DR) strategy to manage real-time power use effectively. Synthetic data tests achieved a 100% acceptance rate with zero reliance on non-renewable power, while real-world tests reduced non-renewable power consumption by over 90%, demonstrating the system’s flexibility. These results provide a foundation for further AI-based heuristics optimization techniques to improve scalability and power efficiency in broader smart city deployments. Full article

This paper evaluates the reliability of smart home energy management systems (SHEMSs) in a residential community with an unreliable power grid and power shortages. Unlike the previous works, which mainly focused on cost analysis, this research assesses the reliability of SHEMSs for different backup power sources, including photovoltaic systems (PVs), battery storage systems (BSSs), electric vehicles (EVs), and diesel generators (DGs). The impact of these changes on the daily cost and the balance of energy source contribution in providing electrical energy to household loads, particularly during power outage hours, is also evaluated. To address the uncertainty of electricity market prices, a risk management approach based on conditional value at risk is applied. Additionally, the study highlights the impact of community size on energy costs and reliability. The proposed model is formulated as a mixed-integer nonlinear programming problem and is solved using GAMS. The effectiveness of the proposed risk-based optimization approach is demonstrated through comprehensive cost and reliability analysis. The results reveal that when electric vehicles are used as backup power sources, the energy index of reliability (EIR) is not affected by market price variations and shows significant improvement, reaching approximately 99.9% across all scenarios. Full article