Hasil untuk "Cybernetics"

N. Packard

Stepan Bosak, Miloslav Capek, Jiri Matas

This paper presents a novel bi-level topology optimization strategy within the method-of-moments paradigm. The proposed approach utilizes an auxiliary variables called edge resistivities related to the Rao–Wilton–Glisson method-of-moments basis functions, for a definition of a fast local optimization algorithm. The local algorithm combines automatic differentiation with adaptive gradient descent. A Bayesian optimization scheme is applied on top of the local algorithm to search for an optimum position of the delta-gap feeding and optimizer hyperparameters. The strength of the algorithm is demonstrated on Q-factor minimization for electrically small antennas. Auxiliary edge resistivity topology optimization outperforms current state-of-the-art topology optimization methods, including material density-based approaches and memetic schemes, in terms of convergence. However, due to the nature of gradient descent, careful tuning of the optimizer hyperparameters is required. Furthermore, the proposed method solves the known binarization issue. Two designs that achieved self-resonance and approached the Q-factor lower bound were further assessed in CST Microwave Studio.

Md. Bipul Hossain, Na Gong, Mohamed Shaban

Semantic segmentation is the area of classifying each pixel in an image using a deep learning model. Examples of widely used semantic segmentation models are the U-Net and DeeplabV3+ models. While the aforementioned models have been deemed very successful in segmenting medical targets including organs and diseases in high resolution images, the computational complexity represents a burden for the real-time application of the algorithms or the deployment of the models on resource-constrained platforms. Until recently, few methods have been introduced for optimizing or pruning of the parameters of the semantic segmentation models. In this paper, we propose two novel channel attention-based filter pruning techniques (i.e., Sub-Sampling Channel Attention (SACA) and Self-Attention Based Attention (SBCA)) in order to reduce the complexity of the semantic segmentation models while maintaining high performance with respect to the benchmark models. This is realized by recognizing the contextual importance of the feature maps in each layer of the models and the significance of each filter to the final model performance. The proposed optimization methods have been validated on the U-Net and DeeplabV3+ models using both lung and skin lesion datasets. The proposed approaches achieved a pruned model performance (i.e., dice coefficient) of up to 96%, as well as an extensively reduced complexity (i.e., percentage of remaining parameters down to 1.1%, model size down to 1.22 MB and number of GFLOPS down to 1.06), outperforming the benchmark magnitude based (i.e., l1-norm, and l2-norm) and the attention-based (i.e., SE, ECA, and CBAM CA) filter pruning methods.

Bohdan Pavlyk, Roman Lys, Roman Gamernyk et al.

Introduction. CsPbBr3 and CsPbCl3 crystals remain among the most intensively studied materials. This is due to their high efficiency in solar cells, which currently exceeds 22%. However, an unsolved problem is the degradation of these perovskites under the influence of external factors. Materials and Methods. This study investigates monocrystals of perovskites CsPbBr3 and CsPbCl3 subjected to X-ray irradiation. The experiment aims to establish the dose-dependent patterns of changes in the electrical characteristics of the crystals. Research under these conditions is relevant due to the high photosensitivity of the materials in the UV, X-ray, and γ-ray spectra, making them promising for the fabrication of photodetectors in the detection of ionizing radiation. Results and Discussion. It has been found that changes in the activation energies of electrical conductivity may result from alterations in the conduction mechanism due to the formation of perovskite phase transitions in CsPbBr3 crystals. A decrease in activation energies of conductivity in the high-temperature range (160–180 °C) was observed, attributed to the effect of X-rays. A trend towards decreasing activation energies of conductivity with increasing dose of irradiation was identified. In the low-temperature range (30–90 °C), CsPbBr3 crystals exhibited sharp changes in current through the samples, significantly decreasing in the range of (95–140) °C, followed by an increase in current at higher temperatures. It is suggested that these results may be attributed to radiation-stimulated generation of vacancy-type defects (VBr) in CsPbBr3 crystals. The formation of similar phase structures in CsPbCl3 crystals occurs at significantly lower temperatures and much lower corresponding thermoelectric currents, necessitating further detailed investigations for their analysis. The generation of radiation defects in CsPbCl3 crystals becomes significant at doses ≥ 130 Gy. Conclusions. The study establishes that the influence of X-ray irradiation and the subsequent passage of current through CsPbBr3 crystals is accompanied by the formation of two perovskite phases, CsPb2Br5 and Cs4PbBr6. The formation of the corresponding phase structures in CsPbCl3 crystals occurs at much lower temperatures and much smaller values of the corresponding thermal currents, therefore, additional, more detailed studies are required for their analysis.

Pavel Filip, Andrej Lasica, Dimitra Kiakou et al.

Introduction: Deep brain stimulation (DBS) of the internal globus pallidus (GPi) is a well-established, effective treatment for dystonia. Substantial variability of therapeutic success has been the one of the drivers of an ongoing debate about proper stimulation site and settings, with several indications of the notional sweet spot pointing to the lower GPi or even subpallidal area. Methods: The presented patient-blinded, random-order study with cross-sectional verification against healthy controls enrolled 17 GPi DBS idiopathic, cervical or generalised dystonia patients to compare the effect of the stimulation in the upper and lower GPi area, with the focus on sensorimotor network connectivity and local activity measured using functional magnetic resonance. Results: Stimulation brought both these parameters to levels closer to the state detected in healthy controls. This effect was much more pronounced during the stimulation in the lower GPi area or beneath it than in slightly higher positions, with stimulation-related changes detected by both metrics of interest in the sensorimotor cortex, striatum, thalamus and cerebellum. Conclusions: All in all, this study not only replicated the results of previous studies on GPi DBS as a modality restoring sensorimotor network connectivity and local activity in dystonia towards the levels in healthy population, but also showed that lower GPi area or even subpallidal structures, be it white matter or even small, but essential nodes in the zona incerta as nucleus basalis of Meynert, are important regions to consider when programming DBS in dystonia patients.

Anatol Godonoaga, Stefan Blanutsa, Borys Chumakov

Introduction. When making decisions under uncertainty, Savage's criterion is sometimes used, or the criterion of minimizing regrets [1]. Usually, in the literature, this decision situation is described in matrix language. In other words, both the number of decision alternatives and the number of states of nature are finite. Of particular interest are situations where the admissible domain of decision variants is a convex set, and the regrets with respect to each state of nature are expressed by convex functions. In this paper, we propose numerical methods for minimizing Savage's regret function, constructed based on the subgradient projection method with automatic step size adjustment [2, 3]. The convergence of these methods is demonstrated. Goal. In the article, the Savage function is defined as a function that expresses the maximum regret value, assumed to be a convex function with respect to the decision factors. This function measures the effectiveness of each decision relative to the set of states of nature. It is important to note that computing the values of these functions is complex because of the need to know the optimal solution for each state of nature. This difficulty is successfully overcome in the process of solving the problem of minimizing functions on convex sets, thanks to parallel solutions of m "internal" algorithms based on the number of states of nature, and one external algorithm, aimed at minimizing the Savage function. Each of the m+1 algorithms represent modifications of the subgradient projection method with a programmable way of adjusting the step size. Depending on the complexity of the constraints and the required precision, three theorems have been proven, confirming the convergence of the investigated methods. Results obtained. Constructive numerical algorithms have been developed for determining optimal decision alternatives under uncertainty, when the number of states of nature is finite, the admissible domain of control factors is convex and compact, and the Savage regret function serves as a decision criterion. The convergence of the corresponding algorithms to the set of optimal solutions has been proven, without knowing the exact values of the Savage function. Instead, estimates obtained from parallel runs of algorithms were used, aimed at determining optimal solutions for each state of nature. Conclusions. Uncertainty poses significant difficulties in designing and making decisions. Any decision made under uncertainty represents a certain risk or a certain regret. In cases where the number of states of nature is finite, the decision domain is convex, the target function with respect to each state of nature is convex, and the Savage regret function is adopted as the decision criterion, the decision-making problem can be successfully solved using numerical algorithms based on the generalized gradient method. The implementation of the algorithm is relatively simple, and the fields of application can be very diverse.

Fuyang Yu, Yu Liu, Zhengxing Wu et al.

The existing fixed gait lower limb rehabilitation robots perform a predetermined walking trajectory for patients, ignoring their residual muscle strength. To enhance patient participation and safety in training, this paper aims to develop a lower limb rehabilitation robot with adaptive gait training capability relying on human–robot interaction force measurement. Firstly, a novel lower limb rehabilitation robot system with several active and passive driven joints is developed, and 2 face-to-face mounted cantilever beam force sensors are employed to measure the human–robot interaction forces. Secondly, a dynamic model of the rehabilitation training robot is constructed to estimate the driven forces of the human lower leg in a completely passive state. Thereafter, based on the theoretical moment from the dynamics and the actual joint interaction force collected by the sensors, an adaptive gait adjustment method is proposed to achieve the goal of adapting to the wearer’s movement intention. Finally, interactive experiments are carried out to validate the effectiveness of the developed rehabilitation training robot system. The proposed rehabilitation training robot system with adaptive gaits offers great potential for future high-quality rehabilitation training, e.g., improving participation and safety.

Alessandra Pietroletti, Alessandro Nicotra

Le innovazioni e le nuove tecnologie possono contribuire enormemente, nel campo della sanità, a migliorare le cure e la ricerca, ma dovrebbero essere regolamentate sulla base di un processo multistakeholder come quello offerto dall’Internet governance. La trasformazione digitale dei sistemi in campo sanitario richiede una particolare attenzione e sensibilità sotto il profilo della protezione e del trattamento dei dati personali. Occorre costruire un ecosistema digitale funzionale, ma sicuro e rispettoso della dignità e dei diritti delle persone.

Camelia Delcea, R. John Milne, Liviu-Adrian Cotfas

The COVID-19 pandemic has produced changes in the entire aviation industry, including adjustments by airlines to keep the middle seats of airplanes empty to reduce the risk of disease spread. In this context, the scientific literature has introduced new metrics related to passengers’ health when comparing airplane boarding methods in addition to the previous objective of minimizing boarding time. As the pandemic concludes and the aviation industry returns to the pre-pandemic situation, we leverage what we learned during the pandemic to reduce the health risk to passengers when they are not social distancing. In this paper, we examine the performance of classical airplane boarding methods in normal times but while considering the health metrics established during the pandemic and new metrics related to passenger health in the absence of social distancing. In addition to being helpful in normal times, the analysis may be particularly helpful in situations when people think everything is normal but an epidemic has begun prior to being acknowledged by the medical scientific community. The reverse pyramid boarding method provides favorable values for most health metrics in this context while also minimizing the time to complete boarding of the airplane.

V.S. Pavelyev, K.N. Tukmakov, A.S. Reshetnikov et al.

Experimental results of the investigation of self-healing properties of terahertz Bessel beams with orbital angular momentum (OAM) with topological charges of l=3 and l=4 in free space after passing through a dispersive medium are presented.

Junyi Chai, Hao Zeng, Anming Li et al.

Deep learning has been overwhelmingly successful in computer vision (CV), natural language processing, and video/speech recognition. In this paper, our focus is on CV. We provide a critical review of recent achievements in terms of techniques and applications. We identify eight emerging techniques, investigate their origins and updates, and finally emphasize their applications in four key scenarios, including recognition, visual tracking, semantic segmentation, and image restoration. We recognize three development stages in the past decade and emphasize research trends for future works. The summarizations, knowledge accumulations, and creations could benefit researchers in the academia and participators in the CV industries.

Mario La Manna

Threat Recognition is a primary task in Homeland Protection systems. When performing this task, Human in the Loop is the main part of a multidisciplinary reasoning process, that allows to achieve a high probability of correct classification. This reasoning process relies on two important factors, namely the past recognition history and the threat scenario. The Human in the Loop agent contributes both in controlling the automated process and in acting as a decision support system in different situations, such as dynamic changes in the scenario and occurrence of anomalous conditions. In this paper, we evaluate the performance of a multidisciplinary system, which uses a combination of a multisensory classification algorithm and a multidisciplinary fusion rule. This fusion rule combines the decisions coming from different channels with the reasoning process of a Human in the Loop agent. The performance evaluation of the multidisciplinary threat recognition system is carried out by considering different case studies. The evaluation demonstrates that a multidisciplinary system with a Human in the Loop agent can classify different threats, by using a set of methods and algorithms, with a high probability of correct classification, when compared to a completely automated recognition criterium.

Alhumyani Hesham

This paper presents an efficient image cipher based on applying the chaotic Baker Map (BM) in the Discrete Cosine Transform (DCT). The encryption module of the proposed DCT-based BM image cipher employs a DCT on the original plain-image then, the DCT coefficients of the plain-image are shuffled with the BM. Finally, the inverse DCT is applied to the shuffled plain-image DCT coefficients to obtain the final cipher-image. The decryption module of the proposed DCT-based BM image cipher employs a DCT on the input cipher-image then, the DCT coefficients of the cipher-image are inversely shuffled with the BM. Finally, the inverse DCT is applied to the inversely shuffled cipher-image DCT coefficients to obtain the original plain-image. A set of experimental tests are performed to test the validity of the proposed DCT-based BM image cipher and the performed tests demonstrated the superiority of the proposed DCT-based BM image cipher in terms of statistical, differential, sensitivity and noise immunity.

Sudipto Basu

In a world-order where planetary computational networks have restructured nearly all spheres of existence, what is not already networked lies in wait merely as standing-reserve. Today, it seems as if the network and the world are naturally interoperable. Thinking through Harun Farocki’s work on operational images, I however locate a zone of friction or incommensurability between the network and the world. Revisiting Norbert Wiener’s anti-aircraft predictor – a founding episode in the history of cybernetics – I show how this gap was bridged by a logic of (en)closures that reduced the living human form and the world to narrow operational ends; banishing the openness and indeterminacy of both life and nature into undesirable contingency. However, cybernetics’ relentless expansion into a universal episteme and planetary infrastructure since the Cold war necessarily floods the network with contingency; which it wards off by feeding on a disavowed living labor. I argue that this living labor is an uneasy reconciliation of mechanism and vitalism, which we may call habits. Drawing on the Marxian notion of general intellect, I posit how habits are key to generating network surplus value, and to cybernetic expansionism. Habits shape, prepare the outside for its subsumption into the network. Yet they are not given the status of productive activity, and consequently disavowed and vaporized by networks. I propose that this living labor be given a specific name – interfacing – and, following Georges Bataille’s critique of political economy, speculate on the reasons for its disavowal. Drawing on Bataille’s idea of the general in ‘general economy’ (that which is opposed to utilitarian or operational ends) and Hito Steyerl’s How Not to Be Seen, I try to imagine what an interface contiguous with the general intellect might be.

Michael Ashby

This paper combines the good regulator theorem with the law of requisite variety and seven other requisites that are necessary and sufficient for a cybernetic regulator to be effective and ethical. The ethical regulator theorem provides a basis for systematically evaluating and improving the adequacy of existing or proposed designs for systems that make decisions that can have ethical consequences; regardless of whether the regulators are humans, machines, cyberanthropic hybrids, organizations, or government institutions. The theorem is used to define an ethical design process that has potentially far-reaching implications for society. A six-level framework is proposed for classifying cybernetic and superintelligent systems, which highlights the existence of a possibility-space bifurcation in our future time-line. The implementation of “super-ethical” systems is identified as an urgent imperative for humanity to avoid the danger that superintelligent machines might lead to a technological dystopia. It is proposed to define third-order cybernetics as the cybernetics of ethical systems. Concrete actions, a grand challenge, and a vision of a super-ethical society are proposed to help steer the future of the human race and our wonderful planet towards a realistically achievable minimum viable cyberanthropic utopia.

G. Fokin

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Benjamin Peters

M. R. Arpentieva

Modern geographical education implies a broad implementation of innovative technologies, allowing students to fully and deeply understand the subject and methods of professional activity, and effectively and productively act upon this understanding. Therefore, in the work of modern geographer computer and media technologies occupy a significant place, and geographic education occupies an important place in learning cybernetic disciplines: computer technologies act as an important condition for obtaining high quality professional education, as well as an important tool of professional activity of modern specialist-geographer. The article is devoted to comparing three modern approaches to the study and optimization of training Cybernetics and programming in the framework of geographical education: an approach devoted to the study of “learning styles”; the metacognitive approach to learning computer science and programming; and intersubjective, evergetic or actually cybernetic, approach. It describes their advantages and limitations in the context of geographical education, as well as the internal unity as different forms of study of productivity and conditions of the dialogical interaction between teacher and student in the context of obtaining high-quality geographical education.