An efficient clustering approach in electrical energy consumption patterns

A comprehensive understanding of electrical energy consumption patterns is essential for strategizing and monitoring the use of energy resources. Industry and business customers of electrical have energy consumption patterns that vary widely depending on the type of industry, business size, and operating hours. This research uses clustering analysis to obtain electrical energy consumption patterns in industrial and business electricity customer groups by grouping data into similar groups. The variables used in this research are daytime, active power (kW), apparent (kVa), and power factor (PF). The objective of this research is to determine the efficacy and benefits of each clustering technique employed in load profile analysis. The clustering algorithm approach used in this research is k-means and fuzzy subtractive clustering (FSC). The trials carried out on these two approaches provide valuable knowledge regarding the effectiveness and superiority of each algorithm in producing significant clusters from the data used in this research. The evaluation conducted using the Davies-Bouldin index (DBI) indicates that the quality value for FSC is 0.25 for business customers and 0.31 for industrial customers. On the other hand, the quality value for k-means is 0.55 for business customers and 0.56 for industrial customers

Continual learning on audio scene classification using representative data and memory replay GANs

This paper proposes a methodology aimed at resolving catastropic forgetting problem by choosing a limited portion of the historical dataset to act as a representative memory. This method harness the capabilities of generative adversarial networks (GANs) to create samples that expand upon the representative memory. The main advantage of this method is that it not only prevents catastrophic forgetting but also improves backward transfer and has a relatively stable and small size. The experimental results show that combining real representative data with artificially generated data from GANs, yielded better outcomes and helped counteract the negative effects of catastrophic forgetting more effectively than solely relying on GAN-generated data. This mixed approach creates a richer training environment, aiding in the retention of previous knowledge. Additionally, when comparing different methods for selecting data as the proportion of GAN-generated data increases, the low probability and mean cluster methods performed the best. These methods exhibit resilience and consistency by selecting more informative samples, thus improving overall performance

Doppler radar-based pothole sensing using spectral features in k-nearest neighbors

Potholes, resulting from wear, weather, and traffic, pose a substantial road safety concern, driving up maintenance costs and government liabilities. Numerous studies have explored pothole detection systems, however, there is a limited focus on radar-based approaches. This study investigates the use of Doppler radar mounted on moving vehicles to collect asphalt road surface data, with the aim to leverage this unique perspective point. Spectral features from power spectral density (PSD) are extracted and explored by incorporating Doppler signal PSD features into a k-nearest neighbors (KNN) within a machine learning framework for road condition classification. Six KNN algorithms are applied, and results indicate that potholes exhibit distinct spectral differences characterized by higher variability, with fine KNN performing the best, achieving an accuracy rate of 95.38% on the test dataset. In summary, this research underscores the effectiveness of Doppler radar-based pothole sensing and emphasizes the significance of algorithm and feature selection for achieving accurate results, proposing the viability of radar systems and machine learning

Hyperparameter tuning for deep learning model used in multimodal emotion recognition data

This study attempts to address overfitting, a frequent problem with multimodal emotion identification models. This study proposes model optimization using various hyperparameter approaches, such as dropout layer, l2 kernel regularization, batch normalization, and learning rate schedule, and discovers which approach yields the most impact for optimizing the model from overfitting. For the emotion dataset, this research utilizes the interactive emotional dyadic motion capture (IEMOCAP) dataset and uses the motion capture and speech audio data modality. The models used in this experiment are convolutional neural network (CNN) for the motion capture data and CNN-bidirectional long short-term memory (CNN-BiLSTM) for the audio data. This study also applied a smaller model batch size in the experiment to accommodate the limited computing resources. The result of the experiment is that the optimization using hyperparameter tuning raises the validation accuracy to 73.67% and the f1-score to 73% on audio and motion capture data, respectively, from the base model of this research and can competitively compete with another research model result. It is hoped that the optimization experiment results in this study can be useful for future emotion recognition research, especially for those who have encountered overfitting problems

Optimizing turbine location in upgraded wind farm using grasshopper optimization algorithm

This research explores the use of the grasshopper optimization algorithm (GOA) for optimizing the placement of additional turbines in an established wind farm. The primary objective is to increase the annual energy production (AEP) of the wind farm while minimizing the wake effects caused by both existing and new turbines. The research evaluates three different turbine types (1.5 MW, 2.0 MW, and 2.5 MW) to identify the most appropriate choice for increasing the wind farm's capacity. The GOA’s performance is compared with the commercial software windPRO and validated using WAsP software for energy calculations. Numerical results indicate that the GOA effectively improves wind farm layout, with the 1.5 MW turbines identified as the optimal choice for maximizing AEP and minimizing wake interactions. This study provides practical insights for wind farm operators and contributes to the development of advanced optimization techniques in wind energy

Intrusion detection system in lightweight devices: issues and challenges

Intrusion detection system (IDS) is a crucial component in ensuring the security of computer networks. It helps in identifying and responding to unauthorized access attempts or malicious activities within a network. The focus of this systematic review is on IDS specifically designed for lightweight devices. This systematic review aims to provide an abstract understanding of the current state of IDSs for lightweight devices. It involves a comprehensive analysis of existing research papers, evaluating the methodologies, techniques, and performance metrics used in these IDS solutions. The goal of the systematic review is to provide a critical assessment and analysis of the literature on IDS in lightweight devices, closing the research gap in this field. The review analyzed and evaluated 55 studies out of 678 initially identified. The findings of the study are presented in the paper, which includes insights into the state-of-the-art proposals in the field, challenges and limitations of existing solutions, and recommendations for future research directions. The outcome of this paper can help the advancement of IDS for lightweight devices

A 0.7 GHz and 0.9 GHz efficient and compact dual-band rectifier for ambient radio frequency energy harvesting

This study introduces a compact dual-band rectifier utilizing a single and multi-stub matching network (MN) technique. The rectifier consists of two branches, each incorporating a single block stub and two blocks stub to generate two frequency susceptance blocks, subsequently transformed into a meandered line. The proposed rectifier operates at two frequency bands of 0.7 GHz and 0.9 GHz and is fabricated on an RT/Duroid 5880 printed circuit board (PCB) with dimensions of 37×25×1.6 mm using an entire ground architecture. Simulation and measurement results show that the rectifier has a power conversion efficiency (PCE) of 67.77% and 66.35% at 0.7 GHz and 70.31% and 71.22% at 0.9 GHz with input power of 0 dBm, respectively. The rectified voltage is 1.79 V DC across a 5 kΩ load terminal (RL) with 5 dBm input power and is capable of sensing low input power down to -30 dBm. This feature makes the rectifier a promising solution for powering low-power devices from ambient energy

Optimization of dynamic transmission network expansion planning using binary particle swarm optimization algorithm

Increasing power demand is usually met by the expansion of generation capacity. The transmission network should be expanded in tandem to ensure power is evacuated from generation points to the load centres. Inadequate power capacity causes congestion. Congestion results due to under-voltages and violation of transmission lines’ loading limits. Constructing additional transmission lines is required to alleviate the congestion after measures of increasing the transmission line’s transfer capability are exploited. Transmission network expansion planning (TNEP) determines the transmission lines to be added to a power system at minimal construction cost, without violating network constraints. In this research, voltage limit violations are penalized in a constrained dynamic TNEP problem for a 10-year planning horizon. The optimal location and number of new transmission lines required at minimal construction cost, and transmission losses associated with the transmission network operations are determined. Improved binary particle swarm optimization (IBPSO) algorithm is applied to optimize the dynamic transmission network expansion planning (DTNEP) results. The developed model is tested on Garver’s 6-bus system using MATLAB. The construction cost for new transmission lines is minimized, and transmission losses reduced when compared to other published works without violating voltage limits (±5%) and transmission lines’ thermal capacities. The transmission network system adequacy is improved

Effect of gamma radiation on semi-crystalline polyvinyl chloride polymer for low-voltage cable insulator

This study explores the properties of semi-crystalline polyvinyl chloride (PVC) polymer as insulation material for low-voltage (LV) cables under high gamma radiation exposure. Test samples underwent gamma radiation (60Co) at doses of 25, 50, 100, 200, 400, and 800 kGy. The evaluation encompassed surface morphology, electrical conductivity, thermal characteristics, and mechanical properties via tensile tests. Electron microscopy observation indicated surface smoothing and flattening occurred at an irradiation dose of 800 kGy. Gamma radiation with increasing doses results in similar thermogram profiles with slight differences in melting temperature and residue mass. The sample irradiated at a gamma dose of 25 kGy generates an increase in the percentage of crystallinity, indicating the occurrence of crosslinking, while other doses exhibit a decrease of crystallinity with increasing radiation dose. Tensile stress significantly dropped up to 400 kGy but increased at 800 kGy. Elongation at break (EAB) decreased with higher gamma radiation doses. Overall, materials up to 800 kGy remained non-brittle, serving as effective insulators and demonstrating thermal stability within high gamma radiation exposure conditions

Modeling 6(10)-35 kV electrical network for fault location via negative correlation

In order to maintain the technical leadership of the economic sector in any nation, there is currently a greater focus on guaranteeing the fail-safe operation of electrical networks and electrical equipment. This paper presents a model for evaluating the fault location procedure based on computer simulation in MATLAB/Simulink of complex 6(10)-35 kV power line systems. The proposed algorithm for preprocessing electrical network signals in normal and emergency modes uses a negative statistical correlation of all possible electrical parameters, while the resulting percentage errors when estimating the location of the fault are within acceptable limits. Algorithms and significant parameters have been determined for effectively carrying out the procedure for searching for the location of a fault through the use of modeling programs, namely: zero-sequence voltage, negative-sequence voltage, initial current value. and the positive sequence voltage is the transition resistance at the accident site. An assessment of the results of preliminary modeling may indicate that devices for finding the location of a fault in the 6(10)-35 kV electrical network will be able to use information obtained about the object using the developed methodology, adjust calculation algorithms and take into account the operating modes of the electrical network