Nowadays, simple and cost-effective solutions to extract flexibility from any possible energy asset are being heavily investigated, along with optimal strategies to offer flexibility in different markets. In this context, this work proposes an Electrical Flexibility Forecasting Engine (EFFE) conceived for district heating systems based on centralised heat pumps. The idea is implemented in the case study of Culemborg (ND), demo site of the H2020-ACCEPT project. Here, the engine is run in a typical winter day to forecast and asses both upwards and downwards flexibility, along with the minimum economically viable bids for a local market.

Power systems are undergoing a significant shift towards renewable energy technologies. To make the most of these energy sources, optimizing the generation, storage and distribution of energy can be enhanced with information about future energy consumption. Forecasting the consumption load of individual residents plays a key role in load balancing but is challenging due to the irregular nature of individual consumption patterns. Further, current literature is limited to forecasting residential load to a few hours in the future. In this paper, we propose Fusion ConvLSTM-Net, a fusion encoder-decoder architecture that combines spatial and temporal features to extend the load forecast to a full 24 hours. We evaluate the model against other benchmark neural network models across various forecast window sizes and average model performance over multiple houses. Additionally, we conduct analyses on its generated forecasts to detect model degradation, which often occurs with a shorter prediction window size. Our extensive experiments demonstrate that the Fusion ConvLSTMNet significantly increases the forecast window, prevents model degradation and delivers the most accurate performance as compared to other benchmark neural networks.

A novel wheelchair called PURE (Personalized Unique Rolling Experience) that uses hands-free (HF) torso lean-to-steer control has been developed for manual wheelchair users (mWCUs). PURE addresses limitations of current wheelchairs, such as the inability to use both hands for life experiences instead of propulsion. PURE uses a ball-based robot drivetrain to offer a compact, self-balancing, omnidirectional mobile device. A custom sensor system converts rider torso motions into direction and speed commands to control PURE, which is especially useful if a rider has minimal torso range of motion. We explored whether PURE’s HF control performed as well as a traditional joystick (JS) human-robot interface and mWCUs, performed as well as able-bodied users (ABUs). 10 mWCUs and 10 ABUs were trained and tested to drive PURE through courses replicating indoor settings. Each participant adjusted ride sensitivity settings for both HF and JS control. Repeated-measures MANOVA tests suggested that the number of collisions, completion time, NASA TLX scores except physical demand, and index of performances were similar for HF and JS control and between mWCUs and ABUs for all sections. This suggests that PURE is effective for controlling this new omnidirectional wheelchair by only using torso motion thus leaving both hands to be used for other tasks during propulsion.Link for supplementary material (appendix, short descriptive video): https://uofi.box.com/s/hbrs0bbbxez4hr51potzywy3p9iupryu

In this paper, a new sampling scheme of the near field radiated by a planar source is proposed and assessed. More in detail, the paper shows a uniform sampling criterion that allows representing the near field over a plane with a number of measurements lower than the classical half-wavelength sampling. At first, a discretization strategy of the near field based on the warping method is recalled from the literature. The latter requires to collect a non-redundant number of field measurements that are non-uniformly arranged over the observation domain. Despite this, the warping sampling scheme works well only if the measurement plane does not overcome the source. When the observation domain is larger, it does not predict the exact positions of the field samples at the edges of the measurement plane; accordingly, in these regions it is not possible to recover the near field behavior by the collected samples. To overcome this drawback, a spatially varying oversampling is exploited. The latter is chosen in such a way that the resulting sampling becomes uniform. Such choice also ensures a growth of the sampling rate only at the edges of the observation domain permitting the retrieval of the near field by its samples. Finally, numerical simulations based on experimental data corroborate the effectiveness of the approach in recovering both the near and the far field.

Anomaly detection is an important task in industrial applications. However, designing an accurate anomaly detector can be very challenging in settings where anomalous labels are sparse or, in the worst case, missing in the training data. To mitigate this issue of a lack of anomalous labels in the domain of interest, existing approaches use transfer learning, leveraging information from anomalous samples in a closely related domain. Although previous studies have shown good results from applying transfer learning, they do not specifically address the issue of high false-positive rates, especially in industrial settings. High false-positive rates can arise from misleading information present in uninformative features. Inspired by this observation, the paper focuses on identifying key input features—termed as such due to their strong predictability in anomaly detection. A transfer learning approach is introduced that leverages the optimal \(f_{\beta}\) score for key feature estimation. This approach involves a weight vector to amplify key features and attenuate uninformative inputs during prediction. We demonstrate the capabilities of our proposed method through an industrial application: anomaly detection for rotating machinery. Based on our findings, anomaly detection algorithms that utilize data-driven features obtained through the proposed method outperform detectors based on features identified by domain experts. More importantly, our proposed framework can work with any downstream unsupervised anomaly detection algorithm, allowing us to freely choose the best algorithm for the anomaly detection task.

California's escalating water shortage, aggravated by ongoing climate change and persistent droughts, necessitates urgent action to preserve this valuable resource. According to the United States Environmental Protection Agency, 50 percent of water used for landscape irrigation and agriculture are wasted through evaporation, wind, and runoff due to overwatering of crops1. Equally important is preventing the under watering of crops, as they can face life-threatening conditions amid California's harsh climate. To strike the delicate balance between water conservation and crop health, this paper explores a method employing soil moisture sensors for precise irrigation control. The sensors measure soil water content, enabling targeted water delivery when levels are low and immediate cessation when optimal moisture is achieved. The system is managed through an Arduino microcontroller, which efficiently regulates the irrigation process based on data gathered through the moisture sensors. The Arduino processes the information received and triggers the water supply, delivered through a pump and a hose. A sprinkler attachment at the end of the hose ensures even water distribution across all plant areas, effectively preventing overwatering in any specific spot. The results indicate over a 45 percent decrease in water use while demonstrating healthier plants. This approach presents a promising solution to California's water scarcity while ensuring sustainable crop growth and efficient resource consumption. The future plans involve using solar energy to power the device's batteries and incorporating artificial intelligence (AI) technologies to detect various factors such as plant species, soil type, terrain, and real-time weather conditions. By leveraging these advanced technologies, the research aims to transform irrigation management for enhanced water efficiency and environmental sustainability concerning California's agricultural practices.

This paper is a continuation on my revolutionary theory of solving the pointwise fluid flow approximation model for time-varying queues. Thus, the long-standing simulative approach has now been replaced by an exact solution by using a constant ratio 𝛽 (Ismail's ratio) , offering an exact analytical solution. The stability dynamics of the time-varying 𝑀/𝐸 𝑘 /1 queueing system are then examined numerically in relation to time, 𝛽, and the queueing parameters.

The paper provides a comprehensive overview of Neural Architecture Search (NAS), emphasizing its evolution from manual design to automated, computationally-driven approaches. It covers the inception and growth of NAS, highlighting its application across various domains, including medical imaging and natural language processing. The document details the shift from expert-driven design to algorithm-driven processes, exploring initial methodologies like reinforcement learning and evolutionary algorithms. It also discusses the challenges of computational demands and the emergence of efficient NAS methodologies, such as Differentiable Architecture Search and hardware-aware NAS. The paper further elaborates on NAS's application in computer vision, NLP, and beyond, demonstrating its versatility and potential for optimizing neural network architectures across different tasks. Future directions and challenges, including computational efficiency and the integration with emerging AI domains, are addressed, showcasing NAS's dynamic nature and its continued evolution towards more sophisticated and efficient architecture search methods.

A time-varying current flows through an inductor. The time-varying magnetic field excites an induced electric field and an induced electromotive force is generated in the coil winding. The supply potential blocks the induced electromotive force from generating conduction current. The time-varying induced electric field excites a magnetic field, forming an electromagnetic wave. A current-carrying conductor and the inductor are fixed together to form a system. The conductor is subjected to an Ampere force in the magnetic field of the electromagnetic wave. Electromagnetic wave is independent of the inductor and independent of the system. The Ampere force is an external force. It can drive the system. Like rocket engines, the system enables propulsion in space. It has the advantage of requiring only electrical power, not propellant.

Distributed training of deep learning models on resource-constrained devices has gained significant interest. Federated Learning (FL) and Split learning (SL) have become the most popular way to do this task. Training data-driven deep learning models in FL/SL involves collaboration between several clients while ensuring user privacy. We aim to optimize these techniques by reducing device computation during parallel model training, and also reducing high communication costs due to model or frequent data and gradient exchanges. This paper proposes Efficient Split Learning (ESL), a novel approach addressing these challenges through three key ideas: (1) a keyvalue store for caching and sharing intermediate activations across clients, significantly reducing redundant computations and communication during the training phase, (2) customization of state-of-the-art neural networks for split learning context, and (3) personalized training allowing clients to learn individual models tailored to their specific data distributions. Unlike previous methods, ESL prioritizes performance optimization while minimizing communication and computation overhead. Extensive experimentation on real-world federated benchmarks for image classification and 3D segmentation demonstrates significant improvements over baseline FL techniques: ESL achieves a reduction in computation by 1623x for image classification and 23.9X for 3D segmentation on resource-constrained devices. Additionally, it reduces communication traffic, during training, between clients and the server by 3.92x for image classification and 1.3x for 3D segmentation, while improving accuracy by 35% and 31%, respectively. Furthermore, when compared to the baseline SL approaches, ESL reduces communication traffic during training by 60x and improves accuracy by an average of 34.8%.

Exploring the fundamental principles of system modeling in electrical engineering, this study delves into the transformative power of the d-q transformation, highlighting its pivotal role in rendering time-varying systems into a coherent steady-state representation. Departing from conventional approaches, the study carefully navigates the complexities of single-phase transformer configurations, utilizing the Clarke and Park transformations to seamlessly transition between electrical coordinates and the d-q frame. Through extensive derivations, dynamic equations are formulated in both α-β and d-q coordinates, providing a detailed understanding of system dynamics under specific loads. In addition, the study extends the analysis to a generalized multi-winding transformer model that accommodates a wide range of transformer setups. With detailed mathematical derivations, insightful visual aids, and clear state-space representations, this work attempt to be a resource for researchers, engineers, and practitioners seeking to unravel the intricacies of electrical system modeling and analysis.

The use of the Choquet integral in multicriteria decision making problems has gained attention in the last two decades. Despite of its usefulness, there is the issue of how to define the Choquet integral parameters, called capacity coefficients, specially the ones associated with coalitions of criteria. A possible approach to address this issue is based on unsupervised learning, which aims to define such parameters with the goal of mitigating undesirable effects provided by intercriteria relations. However, current unsupervised approaches present some drawbacks, as there is no guarantee that the parameters are equally prioritized in the learning procedure. In this paper, we propose a novel unsupervised capacity identification approach which ensures a fair learning for all parameters. Moreover, in comparison with the existing methods, our proposal is less complex in terms of optimization, as it is based on a linear formulation. Experimental results in both synthetic and real datasets attest the applicability of our proposal.

This paper details an experiment utilizing ESP8266 modules as servers to wirelessly control diverse electrical appliances in home automation. The experiment showcased the modules' capability to respond to commands via a web interface on both mobile and desktop platforms or even tablets. While most of the experiment ran smoothly, occasional freezing and connectivity disruptions were observed. The abstract encapsulates the experiment's successes, discusses encountered challenges, and outlines a forward-looking perspective, including the integration of a custom PCB for enhanced system stability.

Integrating the artificial intelligence vision system into robots has significantly enhanced the adaptability of grasping, but are vulnerable to potential backdoor threats. Currently, the majority of backdoor attacks are focused on image classification and are limited to unimodal information and single-object digital scenarios. In this work, we make the first endeavor to realize the backdoor attack on the multimodal vision-guided robot grasping within high-clutter scenarios. Specifically, we propose a novel backdoor attack method named Shortcut-enhanced Multimodal Backdoor Attack (SEMBA), which is divided into two parts. Firstly, for the attack robustness and multimodality, we introduce the Multimodal Shortcut Searching Algorithm (MSSA) to find the pixel value that deviates the most from the mean and standard deviation of the dataset and the pivotal pixel position for individual images, respectively. Next, based on MSSA, we devise the Multimodal Trigger Generation (MTG), to diversify backdoor triggers and realize attacks in the real world. After being trained on this dataset, the model will be activated to prioritize grasping the trigger-like object within the camera view. We conduct extensive experiments on the benchmark datasets and a robotic arm, showing the effectiveness of this method in both the digital and real world. Note to Practitioners-Robots are typically designed to be safe and reliable. However, the integration of artificial intelligence technology with robots can make them unpredictable in certain situations, such as when utilizing third-party data or models. Therefore, it is necessary to explore the security of artificial intelligence-driven robots. In this paper, we address a backdoor attack on robots equipped with an artificial intelligence vision system. Unlike typical backdoor attack methods focused on the digital world, we pay more attention to the attack robustness, multimodality, and adaptability in complex real-world scenarios. Along these lines, we propose a new backdoor attack method and demonstrate its capability to attack multimodality-guided visual grasping systems in high-clutter environments. Our method proposes potential avenues for future research on data-driven security, bringing a wealth of practical insights on a trustworthy visual learning-based robot grasping system.

The conventional use of the Retrieval-Augmented Generation (RAG) architecture has proven effective for retrieving information from diverse documents. However, challenges arise in handling complex table queries, especially within PDF documents containing intricate tabular structures. This research introduces an innovative approach to enhance the accuracy of complex table queries in RAG-based systems. Our methodology involves storing PDFs in the retrieval database and extracting tabular content separately. The extracted tables undergo a process of context enrichment, concatenating headers with corresponding values. To ensure a comprehensive understanding of the enriched data, we employ a fine-tuned version of the Llama-2-chat language model for summarisation within the RAG architecture. Furthermore, we augment the tabular data with contextual sense using the ChatGPT 3.5 API through a one-shot prompt. This enriched data is then fed into the retrieval database alongside other PDFs. Our approach aims to significantly improve the precision of complex table queries, offering a promising solution to a longstanding challenge in information retrieval.

Higher-order boundary conditions are a useful tool for simplification of complex electromagnetic problems, such as spatially dispersive metasurfaces. A technique known as the Extended Generalized Sheet Transition Conditions (GSTCs) method has recently been proposed that allows for the systematic simulation of spatially dispersive metasurfaces using higherorder boundary conditions. A general metasurface, in general requires a boundary condition of infinite order spatial derivatives to accurately model the surface, making them unsuitable for conventional full-wave modeling techniques where higherorder derivatives are computed numerically. Here we propose a mixed spatial-spectral domain representation of the boundary conditions that avoids the explicit computation of higher-order derivatives while still being in a form suitable for integration into standard numerical methods. We integrate these boundary conditions into an integral equation solver which is then successfully validated through several numerical examples.

This work aims at optimizing the converter design of the double-T MMC DC-DC converter in terms of transmitted power per submodule and also in terms of transmitted power per silicon area, while, at the same time, providing the capability to block dc faults. Firstly, the converter operation is described and the optimal values of the inner ac and dc voltages that minimize device power rating are derived. Next, the submodule topology is analyzed and a thorough study on the converter capability for blocking fault currents is carried out, showing that the converter is able to isolate dc faults both at the input and at the output of the converter. Finally, the previous analytical study is verified by means of detailed PSCAD simulations.Copyright (c) 2020 IEEE. Personal use is permitted. For any other purposes, permission must be obtained from the IEEE by emailing [email protected] final version of record is available at http://dx.doi.org/10.1109/TPWRD.2020.2966751

The reengineering of business processes to meet business targets revolves around translating business models into equivalent system models. The failure of many software products has been attributed due to system models not mirroring the business processes they have been developed for. As a result many approaches have been suggested on how to bridge the gap between business processes and systems. This paper presents a case for the use of Business Process Execution Language for Web Services rather than Use-cases in the bridging this gap..