Intelligent control technology and intelligent information integration technology are the two core technologies of intelligent control system. In this paper, intelligent control and intelligent system are studied from two perspectives: multi-agent integration and neural network intelligent control technology. The multi-agent oriented research aims to provide an architecture suitable for information integration of distributed intelligent control systems. The research of neural network aims to deeply study the theory and technology of neural control, an important branch of intelligent control. The main conclusions of this paper are as follows: In the aspect of multi-layer forward neural network learning algorithm, the weight learning and structure learning methods of multi-layer forward neural network are studied. A heuristic genetic algorithm for sample division and a composite structure learning method are proposed. The sample division heuristic genetic algorithm uses the sample division method to construct the initial genetic population, and combines the subspace division for heuristic search. Several algebraic properties of sample set classes and their training are presented and proved.

With the continuous evolution of wireless communication and the explosive growth in data traffic, decentralized spectrum sensing has become essential for the optimal utilization of wireless resources. In this direction, we propose an over-the-air aggregation-based Federated Learning (FL) for a technology recognition model that can identify signals from multiple Radio Access Technologies (RATs), including Wi-Fi, Long Term Evolution (LTE), 5G New Radio (NR), Cellular Vehicle-to-Everything PC5 (C-V2X PC5), and Intelligent Transport Systems G5 (ITS-G5). In the proposed FL-based technology recognition framework, we consider edge network elements as clients to train local models and a central server to create the global model. In each client, a Convolutional Neural Network (CNN)-based model is trained from Inphase and Quadrature (IQ) samples collected from a certain combination of RATs. The possible combination of RATs considered in the clients is selected based on the capabilities of the real-world network elements that can be used as a client. The FL framework involves a process where multiple clients periodically send updates derived from local data to a central server, which then integrates these contributions to enhance a shared global model. This method ensures that the system stays current with the evolving real-world environment while also minimizing bandwidth required for training data transfer and allowing for the maintenance of personalized local models on each clientâ\euro™s end.

Embark on a transformative journey into the realm of Subjective Technologies ( Zero-Input technology)  ! Join our research initiative to explore groundbreaking concepts in user-centric technology, transhumanism, and the fusion of human and machine intelligence. Dive into the future of computational affinity, cognitive amplification, and Subjective Thermo-Currency that renders the concept of money and currencies conceptually obsolete. Contribute to shaping a new era of human-computer interaction that maximizes efficiency, freedom, and potential.Â

Non-autoregressive text-to-speech (TTS) has recently received a lot of atten-tion due to its reliability and fast reasoning. Despite its outstanding achieve-ment, non-autoregressive speech synthesis still faces some critical challenges. A major issue is that non-autoregressive methods necessitate an external toolkit to align the speech with the transcript, thus substantially complicat-ing the process of building the model. Besides, non-autoregressive methods suffer from the one-to-many mapping issue, where the same transcript may correspond to speech in numerous styles. This problem may harm the ex-pressiveness of the generated speech because the model tends to provide output with an average style. To address the above issues, this paper pro-poses a cooperative learning strategy for non-autoregressive speech synthe-sis. Specifically, the suggested method employs both an autoregressive and a non-autoregressive TTS model during the training procedure. The autore-gressive model is trained as a partner at each iteration, providing essential alignment information and also the prosody embedding of the speech to the non-autoregressive model. After receiving the above useful knowledge, the non-autoregressive model can be further trained without relying on external alignment tools. Meanwhile, the prosody embedding from the autoregressive model and the pitch information from the raw audio can be utilised together to alleviate the one-to-many mapping problem. Experimental results demon-strate that our approach can produce comparable speech to the most popular FastSpeech 2 model while drastically reducing the complexity of constructing a non-autoregressive TTS model.

This paper presents a comprehensive framework, which includes a quantification procedure for various Frequency Response Residual-Sources (FRR-Ss), including System Inertia, $M_{sys}$ ( = 2H), System Self-regulation ($D_{self}$), Governor Response ($FR_{Gov.}$), and Effective Governor-Droop ($R_{eff.droop}$) performance. This framework leverages Phasor Measurement Unit (PMU) data, capturing sequence-of-events frequency dynamics from a critical location following a cascaded tripping event. Within this framework, a time duration of 20 sec, starting from the onset of the disturbance, employs suitable approaches to analyze measured frequency in two network cases: ‘Case-I’ real-time simulation in IEEE-24 bus system (RSCAD/RTDS) and ‘Case-II’ real-frequency event measurements from the Indian power system for the aforementioned FRR-Ss quantification. The quantified results for ‘Case-I’ are as follows: $M_{sys}$ = 20.9855 sec, $D_{self}$ = 2.477 p.u, $FR_{Gov.}$ = 10.680 p.u, and $R_{eff.droop}$ = 9.363\%, and results for ‘Case-II’ are 5.0289 sec, 1.2116 p.u, 1.094 p.u, and 91.4076\% respectively. This effort enhances an improved understanding of system frequency dynamics and estimating the frequency response requirements during cascaded tripping events of low-inertia power systems. Finally, the paper provides insights into operational measures and highlights various challenges faced.

To assess the performance of orthomode transducers (OMTs), the back-to-back (BTB) configuration is analyzed in simple fundamentals for accurate results and clear physical insight. The axial rotation of an OMT in the BTB measurement setup is investigated, and clear facts are highlighted. Furthermore, terminating OMTâ\euro™s Common Port with a short circuit load is proposed as a low-cost and accurate evaluation technique based on detailed analysis. The presented techniques have the advantage of using only power measurements without anechoic chambers. A full-wave simulation of a published OMT operating at V-band (55 - 65) GHz is used to validate these analyses.

 Assisted and automated driving (AAD) systems heavily rely on data collected from perception sensors, such as cameras. While prior research has explored the quality of camera data via traditional and well-established image quality assessment (IQA) metrics (e.g. PSNR, SSIM, BRISQUE) or have considered when noisy/degraded data affects perception algorithms (e.g. deep neural network (DNN) based object detection), there are no works that approach the holistic relationship between IQA and DNN performance. This work proposes that traditional IQA metrics, designed to evaluate digital image quality according to human visual perception, can help to predict the sensor data degradation level that perception algorithms can tolerate before performance deterioration occurs. Consequently, a correlation analysis was conducted between 17 selected IQA metrics (with and without reference) and DNN average precision. The evaluated data was increasingly compressed to generate degradation and artefacts. Notably, the experimental results show that several IQA metrics had a strong positive correlation (exceeding correlation scores of 0.7) with average precision, with IW-SSIM and DSS having very high correlation (> 0.9). Interestingly, the results show that re-training BRISQUE on compressed data causes an exceptionally high positive correlation (> 0.97), making it very suitable for predicting the performance of DNN object detectors. By effectively relating traditional image quality metrics to DNN performance, this research offers a series of significant tools to understand and predict perception degradation based on the quality of data, thus resulting in a significant impact on the development of automated driving systems.Â

An accurate assessment of a mission system’s performance, called Mission Impact Assessment (MIA), can effectively identify and counteract any issues in the current system to avoid potential risks or vulnerabilities that may cause mission failure. Although the importance of MIA research has been recognized to mitigate the system’s potential risk for more than a decade, little research has shown a comprehensive MIA framework with experimental validation showing the inference performance of the MIA tools under realistic attack-defense interactions. To fill this gap, we propose an interdependent mission impact assessment (MIA), called iMIA, that can adequately capture the inter-relationships of the key components in a mission system and environment and accurately infer a mission outcome (i.e., success or failure) under uncertainty.  In the proposed iMIA, we first consider Subjective Bayesian Networks to evaluate how successfully a given system introduces impacts on mission success under uncertainty due to a lack of evidence.  In addition, we consider strategic attack-defense interactions based on a hypergame theory in which the attacker and defender can take actions under their perceived uncertainty.  Our extensive experiments showed the outperformance of our proposed MIA up to 70% and 25% over the performance of the baseline and the state-of-the-art Bayesian Network-based counterparts, respectively, in terms of inference accuracy in mission performance (e.g., mission outcomes, such as mission success or failure).  We also provide in-depth sensitivity analyses to identify the key system components that should be considered more carefully to avoid mission failure.

The flashover in vacuum is a rapid interfacial discharge across the insulator surface when subjected to high applied voltage. Here a theoretical model covering above-surface processes is introduced. The model calculates the flashover threshold in vacuum, with revised secondary electron emission avalanche theory and improved desorbed neutral transport model. The model serves as the first part of an integrated flashover model, aiming for consistent treatment of both above-surface and subsurface processes during the entire flashover development. The flashover threshold is obtained by combining the desorbed neutral pressure at given applied voltage and the gas breakdown criterion dictated by the Paschenâ\euro™s law. An analytical formula for threshold estimation containing physical parameters, and a simplified formula consisting of empirical coefficients are introduced, catering for both conceptual understanding and practical application. The derived formulae are validated by experimental data for a variety of insulator materials. The theory generalization for nonuniform electric field distribution is further discussed.

The paper aims to try to push the current conecpt about reduced switch count Multi-level inverter (RSC-MLI) for battery sotage systems. They can be used to reduce the count of Mosefts used in Home storage systems(HSS); However, it would require precise Voltage measuring instruments and a complexer code to control the switching of Mosfets. To smooth the on /off step function, one can also use an inductor. The research sparked interest during Covid phase when the prices of certain Mosfets increased 20 folds.Â

This abstract delves into the intricate relationship between the abstract concept of intelligence and its embodiment, drawing attention to the lack of coherence in understanding intelligence and its implications for artificial intelligence (AI). Intelligence, inherently grounded in human experience, attempts to transcend its embodiment, posing challenges for the development of artificial general intelligence (AGI). The concept of embodiment extends beyond physical instantiation, encompassing the interdependence of an autopoietic system on its environment. The pursuit of autonomy and general capability in AGI necessitates the recreation of the organism’s natural condition of embodiment. However, the feasibility , controllability, and overall advantages of such artificial embodiment remain uncertain. This abstract explores the complex interplay between intelligence, embodiment, and the quest for AGI, raising critical questions about the path forward in the development of intelligent systems.Â

Depression severity can be classified into distinct phases based on the Beck depression inventory (BDI) test scores, a subjective questionnaire. However, quantitative assessment of depression may be attained through the examination and categorization of electroencephalography (EEG) signals. Spiking neural networks (SNNs), as the third generation of neural networks, incorporate biologically realistic algorithms, making them ideal for mimicking internal brain activities while processing EEG signals. This study introduces a novel framework that for the first time, combines an SNN architecture and a long short-term memory (LSTM) structure to model the brainâ\euro™s underlying structures during different stages of depression and effectively classify individual depression levels using raw EEG signals. By employing a brain-inspired SNN model, our research provides fresh perspectives and advances knowledge of the neurological mechanisms underlying different levels of depression. The methodology employed in this study includes the utilization of the synaptic time dependent plasticity (STDP) learning rule within a 3-dimensional braintemplate structured SNN model. Furthermore, it encompasses the tasks of classifying and predicting individual outcomes, visually representing the structural alterations in the brain linked to the anticipated outcomes, and offering interpretations of the findings. Notably, our method achieves exceptional accuracy in classification, with average rates of 98% and 96% for eyes-closed and eyes-open states, respectively. These results significantly outperform state-of-the-art deep learning methods.

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The research addresses a critical aspect of organizational settings â\euro“ team formation â\euro“ which is vital for effective team-based learning. In many organizational environments, successful teamwork depends on the composition of teams that consider various criteria, such as skill levels, background, and personality traits. Meanwhile, maintaining fairness and equity among teams is crucial to ensure equal opportunities for all team members. Our research framework lies in its ability to model a diverse range of factors, while simultaneously maximizing within-team diversity and minimizing conflict levels. In this study, we introduce a novel application of multi-criteria integer programming (MCIP) that diverges from traditional and single-objective optimization methods to address the complexity of team formation with a focus on multiple criteria. This approach offers a comprehensive solution that accommodates multiple criteria simultaneously. The contributions of this paper are: 1) An innovative optimization model for team formation that addresses the complex task of maximizing intra-group diversity while minimizing inter-group diversity. 2) Addressing the challenge of forming balanced and diverse student teams and providing educators with a practical tool for effective team formation.

Radar systems are gaining in popularity and spreading into more and more applications due to their robust measurement method. While single-chip solutions have been published in the last decade in the upper millimetre-wave range, solutions are still very limited in the lower THz range. In this paper, a 0.48THz FMCW-radar transceiver MMIC in a 130nm silicon-germanium (SiGe) technology is presented. The MMIC consists of one Tx and Rx channel, respectively. The components for frequency synthesis and on-chip patch antennas are fully integrated into the MMIC. The achievable tuning range of the MMIC is 0.448 − 0.491 THz with a peak on-chip output power of about -9.4 dBm. The MMIC was designed and manufactured with the IHP SG13G3 technology.

We review the transformer architectures that have potentially paved the way toward providing it with a better cognitive ability. This has led these architectures to think and query themselves with higher levels of intelligent questions and provide relevant answers by evolving subject-context relationships in the transformer layers. This is very similar to the cognitive ability of the human mind which can potentially think creatively by asking more relevant questions to the problem at hand which we frequently call “thinking out loud”. These transformer architectures are potentially thought for a more creative and deep thinking approach by doing repeated queries and monitoring generated responses for cognitive behavior. Cognition and thinking for creating better answers in a generative AI system are evolving rapidly. In this article, we are trying to summarize the research conducted and the systems that evolved over time for cognitive thinking (after the basic architecture was proposed).

Sensorimotor impairment is a prevalent condition requiring effective rehabilitation strategies. This study introduces a novel wearable device for Mindful Sensorimotor Training (MiSMT) designed for sensory and motor rehabilitation. Our MiSMT device combines motor training using myoelectric pattern recognition along sensory training using two tactile displays. This device offers a comprehensive solution, integrating electromyography and haptic feedback, lacking in existing devices. The device features eight electromyography channels, a rechargeable battery, and wireless Bluetooth or Wi-Fi connectivity for seamless communication with a computer or mobile device. Its flexible material allows for adaptability to various body parts, ensuring ease of use in diverse patients. The two tactile displays, with 16 electromagnetic actuators each, provide touch and vibration sensations up to 250 Hz. In this proof-of-concept study, we show improved two-point discrimination after 5 training sessions in participants with intact limbs (p=0.047). We also demonstrated successful acquisition, processing, and decoding of myoelectric signals in offline and online evaluations. In conclusion, the MiSMT device presents a promising tool for sensorimotor rehabilitation by combining motor execution and sensory training benefits. Further studies are required to assess its effectiveness in individuals with sensorimotor impairments. Integrating mindful sensory and motor training with innovative technology can enhance rehabilitation outcomes and improve the quality of life for those with sensorimotor impairments.

In recent decades, microwave photonic channelization has developed rapidly. Characterized by low loss, high versatility, large instantaneous bandwidth, and immunity to electromagnetic interference, microwave photonic channelization techniques coincides with the requirements of modern radar and electronic warfare for receivers. The advanced microresonator-based optical frequency combs are promising platforms for the research of the photonic channelized receivers, which take full advantage of multicarrier and large bandwidth, and accelerate the integration process of microwave photonic channelized receivers. In this paper, we review the research progress and trends in microwave photonic channelization, focusing on schemes that utilize integrated microcombs. Finally, we discuss the potentials of microcomb-based RF channelization, as well as the challenges and limitations, and provide routes and perspectives for future developments in the context of on-chip silicon-based photonics.Â