Whereas robots are and will be increasingly present in all areas of society, robotics can be limited by its narrow perspective, often neglegecting discussions about its own goals, epistemology, ethics, and socio-political impacts. In order to try to adress this issue, this article proposes a holistic analysis of robots and robotics. Taking into these blind spots, raising new challenges. In response, the second part proposes a set of guidelines : a new epistemology, a metaethical framework, an organization method and a template design in order to answer these challenges.
In an era where the drumbeats of technological advancement echo through the corridors of military strategy, my research takes a deep dive into the storied pasts of military legends-General John J. Pershing, General George S. Patton Jr., and General Norman Schwarzkopf-to juxtapose their timeless strategies with the burgeoning field of Artificial Intelligence (AI) in warfare. This comparative analysis, crafted with the meticulousness of a New York Times feature, seeks to unravel the complex tapestry of leadership, tactical innovation, and the human element that defined the battlegrounds of the 20th century, and to critically examine how AI might redefine the very fabric of military operations in the future.
Sharing basic safety messages (BSMs) among connected vehicles (CVs) in a timely and reliable manner is of paramount importance in vehicular networks. When CVs are connected through ad hoc networks, the timely delivery of BSMs is very challenging due to the randomness in medium access control (MAC) and may lead to collision, especially in crowded networks. Besides, although the channel acquisition in conventional methods via transmission and reception of control signals results in collision-free message delivery, it adds high overhead cost. In this paper, we propose an efficient MAC scheme to carefully address these issues by improving communication efficiency and reducing the signaling overhead. The proposed scheme dedicates each time slot to only one CV and consequently is collision-free. Since BSMs contain similar information of a CV, we adopt the age of information (AoI) as the performance metric. We derive mathematical expressions for the MAC delay and AoI of the collision-free scheme by proposing a two dimensional Markov model. We compare the performance of proposed scheme with IEEE 802.11p standard and another lowcomplexity random scheme. AoI, delay, and collision rate are evaluated by OPNET network simulator, which provides realworld implementation scenario. Simulations results show that the collision-free scheme performs significantly better than IEEE 802.11p in highly congested networks. As an example, for a dense scenario where BSMs are generated every 10 ms, AoI of collisionfree scheme is about 50 ms while those of IEEE 802.11p and random scheme are about 140 and 150 ms, respectively, which are assumed too high for safety applications. Besides, the results show almost perfect match between mathematical derivations and results obtained by OPNET.
Incisive selection of the LCL filter parameters for a grid-connected inverter (GCI) is crucial to meet the grid interconnection standards with a reduced hardware footprint. Various design methods are available in the literature for selecting the LCL filter parameters. While the grid-side inductor of the LCL filter can utilize an iron core and follow the standard grid frequency inductor design, the inverter-side inductor design needs attention since it has significant switching frequency harmonics. This paper presents an extensive discussion on the design of the inverter-side inductor for GCIs. The inverter-side inductor (L i) is calculated based on the allowable inverter peak-peak ripple current to reduce the losses due to the ripple component. The value or size of L i depends on the inverter configuration, switching technique, and the application. The initial sections of the paper present a comprehensive analysis, comparing the value and hence the size of L i for different wiring configurations and applications. Closed-form expressions are developed for L i and are used in selecting the minimum value of L i. The suitability of an amorphous core for the inverter-side inductor is discussed. The amorphous-core inductor designs in literature can lead to a wide variation of inductance with current and have been analyzed to cause differential and common mode noise. To address this, a novel amorphous-core inductor design is proposed in the later sections of this work. The proposed approach ensures a minimal variation in the inductance over the operating current range. Experimental results are provided to support the various theoretical assertions.
A load-independent constant current (CC)-constant voltage (CV) output is an important requirement of inductive power transfer (IPT) systems for electric vehicle charging applications. Zero phase angle (ZPA) is also a desirable feature, to ensure a lower power rating requirement for the switching converter. CC and CV output along with ZPA can be achieved by using a suitable compensation topology. Equation manipulation techniques can be used for designing the compensation topology. But, it can be mathematically intensive especially for higher order topologies. To overcome this problem, resonant-tank based approaches are adopted in several works to achieve CC and CV conditions. However, equation-based approaches are depended upon either wholly or partly for realizing ZPA. This approach can be tedious and lacks physical insight. The proposed method extends resonant tank approach to achieve ZPA also, besides CC and CV. The need for a separate method to achieve ZPA is eliminated. Further, it simplifies the process in arriving at the constraints that ensure ZPA. As a sample validation, the proposed method is applied to a S-SP compensation topology. The CC-ZPA and CV-ZPA constraints for the S-SP topology are shown to be in line with the ones arrived at using an existing equation-based impedance approach. The simplicity of the proposed method can be observed from the sample validation.
The Internet of Things (IoT) enables billions of smart devices to capture, process, and transform data to improve decision-making. IoT demands a critical mobile edge computing (MEC) ecosystem to provide dependability. It requires an efficient and distributed architecture with multiple IoT communication protocols. In this way, intelligent middleware is needed to achieve the efficiency, throughput, and reliability of data delivery on different protocols without interference from the local setup of the device. This paper proposes a modular and interoperable middleware called MiddleFog to select the most appropriate communication protocol among MQTT and CoAP dynamically. Also, the approach minimizes communication limitations caused by latency, package loss, and low network throughput between MEC and Cloud. The initial evaluations show a message loss rate lower than 25% for small messages, and performance improves around 48% for medium-sized delivery messages.
The Collatz conjecture, a longstanding mathematical puzzle, posits that, regardless of the starting integer, iteratively applying a specific formula will eventually lead to the value 1. This paper introduces a novel approach to validate the Collatz conjecture by leveraging the binary representation of generated numbers. Each transition in the sequence is predetermined using the Collatz conjecture formula, yet the path of transitions is revealed to be intricate, involving alternating increases and decreases for each initial value. The study delves into the global flow of the sequence, investigating the behavior of the generated numbers as they progress toward the termination value of 1. The analysis utilizes the concept of probability to shed light on the complex dynamics of the Collatz conjecture. By incorporating probabilistic methods, this research aims to unravel the underlying patterns and tendencies that govern the convergence of the sequence. The findings contribute to a deeper understanding of the Collatz conjecture, offering insights into the inherent complexities of its trajectories. This work not only validates the conjecture through binary representation but also provides a probabilistic framework to elucidate the global flow of the sequence, enriching our comprehension of this enduring mathematical mystery.
Chemical process design is the search for an optimal manufacturing protocol to perform chemical operations. For transient processes such as crystallization, the optimal conditions can change over time, requiring a dynamic strategy. Model-free deep reinforcement learning is an approach that can be used to identify the best sequence of states with respect to a predefined reward function. In this work, proximal policy optimization is applied in a simulated environment to identify operational strategies that are optimal with respect to the desired particle properties in unseeded batch cooling crystallization processes of paracetamol in ethanol. For this purpose, the corresponding Markov decision process is formulated, and it is shown that the method is promising for the development of novel routes that allow the tuning of particle size (623 μm) and provide high yields (96%) within a defined period of time (12 h).
Motor unit (MU) decomposition, generally, requires a time-consuming and labor-intensive manual inspection/editing process from human operators to ensure high accuracy. In this study, we propose and validate a rule-based auto-editing method that could potentially substitute the manual process. Methods: The proposed auto-editing framework (autoeditor) consists of four main rules for adding and removing spikes based on the height of the innervation pulse train (IPT) and the regularity of the firing rate of the identified motor unit. The rules were optimized and validated based on an open-source database including raw MU spike trains estimated from the convolution kernel compensation method and the manually edited MU spike trains from eight human operators. Results: Across 110 motor units, the average rate of agreement between the auto-editor and human operators reached 99.2% after the auto-editor corrected more than 10 edits for each motor unit on average from the raw spike trains. More importantly, the characteristics of the motor unit behaviors, including the MU firing rate and recruitment threshold, were consistent across human operators and the proposed auto-editor. Conclusion: With a simple but effective rulebased auto-editing framework, comparable performance in MU refinement was achieved as human operators. Significance: The proposed auto-editing framework has the potential to standardize the MU editing practice, lower the requirements for expert knowledge and specialized training for MU decomposition, and provide an expandable framework allowing contributions from the community.
Horizontal Federated Learning exhibits substantial similarities in feature space across distinct clients. However, not all features contribute significantly to the training of the global model. Moreover, the curse of dimensionality delays the training. Therefore, reducing irrelevant and redundant features from the feature space makes training faster and inexpensive. This work aims to identify the common feature subset from the clients in federated settings. Banerjee et al. introduced Fed-FiS 1 , and here we propose a hybrid approach known as Fed-MOFS, where Mutual Information and Clustering are used to select local features from each client. In both approaches, the selection of local features is similar, but Fed-FiS uses a scoring function to evaluate the global ranking of each feature, while Fed-MOFS exploits multi-objective optimization to rank the features based on their higher relevance and lower redundancy criteria. We select the feature subset based on the global ranks for learning. Empirically, we evaluated the performance, stability, and efficacy of Fed-FiS and Fed-MOFS on 12 datasets. We compared Fed-FiS and Fed-MOFS with conventional techniques such as ANOVA and RFE and a federated feature selection method called FSHFL. The experimental results demonstrate both Fed-FiS and Fed-MOFS improve the performance of the global model even after 50% reduction in the feature space size. Both Fed-FiS and Fed-MOFS are at least 2× faster than FSHFL. Also we verified the effect of feature selection on the convergence of the global model. The computational complexity of Fed-FiS and Fed-MOFS is O(d2) and O(2d2), respectively, which is lower than state-of-the-art.
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.
The power system is undergoing a large change towards renewable energy technologies. While using these energy sources, managing the generation, storage and distribution of energy can be optimized with information about future energy consumption. The forecasting of consumption load for individual residents plays a key role for load balancing but is a challenging task due to the volatile nature of individual consumption. Due to this reason, current literature has only been limited to forecasting individual load to a small window in the future. In this paper, we introduce a Sequence-To-Sequence Long Short-Term Memory (LSTM) Recurrent Neural Network (RNN) framework to generate a 24 hour load forecast. We show comparisons with other deep neural network models of 1) Model performance over varying forecast window sizes, 2) Average model performance over multiple houses and 3) Performance for forecasting the aggregated load of all houses. We also conduct analyses on the forecasts to show performance improvement for households with consistent load patterns and to detect model degradation. Our extensive experiments show that the Sequence-To-Sequence LSTM RNN can significantly increase the forecast window and performs best for all scenarios.
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.
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.