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AbstractThis article examines the afterlives of state classificatory categories in everyday engagements between state and society. It examines how a community of cultivators in eastern and central India, the Kudmi-Mahatos, have reshaped their group identity through categories of the state. They have oscillated between not wanting to be labelled as “tribal” by the colonial state to seeking to be recognised as “Scheduled Tribes” by the post-colonial state. Using ethnographic and historical methods, this article investigates the shifting claims of the Kudmi-Mahatos with respect to “tribal” status. It argues: (i) that communities use state categories of classification to remake their identities vis-à-vis both the state and other communities they live with; and (ii) tribalisation as a strategy of claiming recognition reworks the tribe-caste continuum in India. The article builds on historical and anthropological scholarship that looks at political contestations over classification in modern states. [ABSTRACT FROM AUTHOR]

The article presents the possibility of using machine learning (ML) in artificial intelligence to classify the technical state of marine engine injectors. The technical condition of the internal combustion engine and injection apparatus significantly determines the composition of the outlet gases. For this purpose, an analytical package using modern technology assigns experimental test scores to appropriate classes. The graded changes in the value of diagnostic parameters were measured on the injection subsystem bench outside the engine. The influence of the operating conditions of the fuel injection subsystem and injector condition features on the injector needle vibration displacement waveforms was subjected to a neural network (NN) ML process and then tested. Diagnostic parameters analyzed in the amplitude, frequency, and time–frequency domains were subjected after a learning process to recognize simulated various regulatory and technical states of suitability and unfitness with single and complex damage of new and worn injector nozzles. Classification results were satisfactory in testing single damage and multiple changes in technical state characteristics for unfitness states with random wear injectors. Testing quality reached above 90% using selected NNs of Statistica 13.3 and MATLAB R2022a environments. [ABSTRACT FROM AUTHOR]

Transformers play a crucial role in power systems. In this respect, fault diagnosis and aging state assessment have garnered significant attention from researchers. Herein, accelerated thermal aging and Raman scattering experiments are conducted on oil–paper insulation samples to accurately detect aging states. The samples are categorized into different aging stages based on the polymerization degree of the insulating paper. Principal component analysis (PCA), multi-dimensional scale change method (MDS), and isometric mapping algorithm (Isomap) are employed to extract features from the Raman spectra. Subsequently, the XGBoost strong classifier, optimized through Bayesian hyperparameter optimization (BO-XGBoost), is utilized to distinguish between four and ten states among 175 groups of samples after feature extraction. The subsequent classification results of the three feature-extraction methods are compared. The results indicate that Isoamp, which pertains to the manifold dimension transformation, achieves the highest average discriminative accuracy after feature extraction. The discriminative accuracies for aging states four and ten are 97.0% and 95.1% respectively, demonstrating that Raman spectroscopy manifold dimension transformation enhances the distinctiveness between samples of different aging states in the feature-extraction process. The diagnostic model constructed with Isomap and BO-XGBoost enables accurate discrimination of the aging states of oil–paper insulation. [ABSTRACT FROM AUTHOR]

Photovoltaic (PV) array, as the key component of large-scale PV power stations, is prone to frequent failure that directly affects the efficiency of PV power stations. Therefore, accurate classification of the operating state of PV arrays is the basis for fault location. Thus, a novel classification method for PV array operating state was designed based on nonparametric estimation and a 3σ method. The actual data analysis proves the hypothesis that performance ratio (PR) distribution characteristics of PV arrays can characterize the operating state of PV arrays. The modeling curve of the PV array with an excellent performance has only one peak and the peak value is large, while the distribution curve of the PV array with a poor performance has a small peak. In this paper, the distribution characteristics of PV arrays are modeled, the peak value is used to classify the operating state of PV arrays, and finally the effectiveness of the proposed method is compared. Overall, this paper makes a valuable contribution by proposing a novel method for accurately classifying the operating state of PV arrays. The proposed method can help improve the efficiency and fault diagnosis of PV power stations. [ABSTRACT FROM AUTHOR]

Summary: Water pipeline condition monitoring, especially that of potable water pipeline, has attracted more research interest due to the increasing public concern in pipeline serviceability. This study focuses on the condition monitoring and temporal‐spatial assessment of potable‐water‐filled composite pipeline by using a network of specially designed fiber Bragg grating (FBG) sensors. In responding to the challenges imposed by the operational environment in the pipeline, the sensory network development is guided by application‐specific design with three phases, including suitable sensor development, applicability validation, and implementation. A 200‐m‐long buried fiber reinforced plastic (FRP) pipeline for conveying potable water is instrumented and the in‐service pipeline has been monitored mainly by the properly designed and fixed strain sensors for nearly 2 years, forming a database with its data acquired in different conditions. Furthermore, targeting at the difficulty in the online anomaly identification of longitudinally extended structure, this study proposes a temporal‐spatial condition assessment scheme for identifying the anomalous time slots and locations of pipeline. This scheme consists of an offline modeling and validation process taking advantages of random forest (RF) algorithm for serviceability‐state classification, an online temporal‐spatial assessment process featured by a two‐stage assessment strategy which employs the temporal and then the spatial RF offline models for online state identification, and a reporting mechanism based on the analysis of anomaly types. The performance of the proposed state assessment scheme is examined by using the database, and its effectiveness is demonstrated by the high accordance between the identified results and the real conditions of the pipeline. [ABSTRACT FROM AUTHOR]

The article presents the possibility of using machine learning (ML) in artificial intelligence to classify the technical state of marine engine injectors. The technical condition of the internal combustion engine and injection apparatus significantly determines the composition of the outlet gases. For this purpose, an analytical package using modern technology assigns experimental test scores to appropriate classes. The graded changes in the value of diagnostic parameters were measured on the injection subsystem bench outside the engine. The influence of the operating conditions of the fuel injection subsystem and injector condition features on the injector needle vibration displacement waveforms was subjected to a neural network (NN) ML process and then tested. Diagnostic parameters analyzed in the amplitude, frequency, and time–frequency domains were subjected after a learning process to recognize simulated various regulatory and technical states of suitability and unfitness with single and complex damage of new and worn injector nozzles. Classification results were satisfactory in testing single damage and multiple changes in technical state characteristics for unfitness states with random wear injectors. Testing quality reached above 90% using selected NNs of Statistica 13.3 and MATLAB R2022a environments.

Transformers play a crucial role in power systems. In this respect, fault diagnosis and aging state assessment have garnered significant attention from researchers. Herein, accelerated thermal aging and Raman scattering experiments are conducted on oil–paper insulation samples to accurately detect aging states. The samples are categorized into different aging stages based on the polymerization degree of the insulating paper. Principal component analysis (PCA), multi-dimensional scale change method (MDS), and isometric mapping algorithm (Isomap) are employed to extract features from the Raman spectra. Subsequently, the XGBoost strong classifier, optimized through Bayesian hyperparameter optimization (BO-XGBoost), is utilized to distinguish between four and ten states among 175 groups of samples after feature extraction. The subsequent classification results of the three feature-extraction methods are compared. The results indicate that Isoamp, which pertains to the manifold dimension transformation, achieves the highest average discriminative accuracy after feature extraction. The discriminative accuracies for aging states four and ten are 97.0% and 95.1% respectively, demonstrating that Raman spectroscopy manifold dimension transformation enhances the distinctiveness between samples of different aging states in the feature-extraction process. The diagnostic model constructed with Isomap and BO-XGBoost enables accurate discrimination of the aging states of oil–paper insulation.

State modeling of whole-brain electroencephalography (EEG) or magnetoencephalography (MEG) allows to investigate transient, recurring neurodynamical events. Two widely-used techniques are the microstate analysis of EEG signals and hidden Markov modeling (HMM) of MEG power envelopes. Both reportedly lead to similar state lifetimes on the 100 ms timescale, suggesting a common neural basis. To investigate whether microstates and power envelope HMM states describe the same neural dynamics, we used simultaneous MEG/EEG recordings at rest and compared the spatial signature and temporal activation dynamics of microstates and power envelope HMM states obtained separately from EEG and MEG. Results showed that microstates and power envelope HMM states differ both spatially and temporally. Microstates reflect sharp events of neural synchronization, whereas power envelope HMM states disclose network-level activity with 100–200 ms lifetimes. Further, MEG microstates do not correspond to the canonical EEG microstates but are better interpreted as split HMM states. On the other hand, both MEG and EEG HMM states involve the (de)activation of similar functional networks. Microstate analysis and power envelope HMM thus appear sensitive to neural events occurring over different spatial and temporal scales. As such, they represent complementary approaches to explore the fast, sub-second scale bursting electrophysiological dynamics in spontaneous human brain activity.

Objective: An important EEG-based biomarker for epilepsy is the phase-amplitude cross-frequency coupling (PAC) of electrical rhythms; however, the underlying pathways of these pathologic markers are not always clear. Since glial cells have been shown to play an active role in neuroglial networks, it is likely that some of these PAC markers are modulated via glial effects. Methods: We developed a 4-unit hybrid model of a neuroglial network, consisting of 16 sub-units, that combines a mechanistic representation of neurons with an oscillator-based Cognitive Rhythm Generator (CRG) representation of glial cells—astrocytes and microglia. The model output was compared with recorded generalized tonic-clonic patient data, both in terms of PAC features, and state classification using an unsupervised hidden Markov model (HMM). Results: The neuroglial model output showed PAC features similar to those observed in epileptic seizures. These generated PAC features were able to accurately identify spontaneous epileptiform discharges (SEDs) as seizure-like states, as well as a postictal-like state following the long-duration SED, when applied to the HMM machine learning algorithm trained on patient data. The evolution profile of the maximal PAC during the SED compared well with patient data, showing similar association with the duration of the postictal state. Conclusion: The hybrid neuroglial network model was able to generate PAC features similar to those observed in ictal and postictal epileptic states, which has been used for state classification and postictal state duration prediction. Significance: Since PAC biomarkers are important for epilepsy research and postictal state duration has been linked with risk of sudden unexplained death in epilepsy, this model suggests glial synaptic effects as potential targets for further analysis and treatment. [ABSTRACT FROM AUTHOR]

Path following control of the automatic guided vehicle (AGV) for material handling in the plant requires a smooth and high-precision continuous attitude adjustment. However, there are many difficulties in designing the controller, such as actuator saturation, parameter selection, and the influence of deviations relations. In this paper, an improved model predictive control SCMPC-ST with a state classification model (SCM) and a smooth transition (ST) strategy is proposed to address these problems. First, based on the deviations relations and the actuator saturation, the SCM is designed to divide the pose states of AGV into three stages. With clearly objective functions and boundary constraints, SCM allows the pose states can be transferred sequentially, which avoids the problem of parameter selection. Second, use the analytical method to resolve SCM to directly obtain the desired control law, which provides excellent performance in real-time. Finally, the smooth transition strategy is built to adjust the control law at the transition step and overshoot step to ensure the stability of the control process. Simulation results show that by focusing on adjusting the deviations relations, SCMPC-ST can make AGV eliminate all deviations continuously and smoothly while maintaining high accuracy. [ABSTRACT FROM AUTHOR]

Despite the widespread attention to capital investments in land and property around the globe, the active re‐regulating role of the neoliberal state in processes of "accumulation by dispossession" remains underexplored. Through an in‐depth look at the dispossession of highly fragmented and loosely regulated private land for windfarm investments on Crete's eastern corner, Sitia, this paper re‐affirms the political nature of the forcible appropriation of land for large‐scale investments; dissects the specific mechanisms in which the state dispossesses land on behalf of investors and promotes the forcible appropriation of land from below; and problematises the dialectic relationship of both rupture and continuity between crisis and inherited, path‐dependent relations embedded in land. The transformation of Sitia's loosely regulated, informal relations on land is made possible through the mobilisation of the state's bureaucratic and normalising powers, which redefine the concept of forest and dispossess through classifying land as such. [ABSTRACT FROM AUTHOR]

Many states use a classificatory approach to foreign policy: they put other states into particular categories and structure their engagement and relations partly as a result. There is one prominent modern international political theory – Rawls’ Law of Peoples – that seems to adopt this approach as an account of justified state behaviour. But should we expect this type of theory ultimately to prove attractive, justified and philosophically distinct compared to more instrumentalist rivals? This paper explores the challenges generic to any such account, not merely those relating to Rawls’ specific version, and surveys possible responses and their shortcomings.

The task of classifying the entanglement properties of a multipartite quantum state poses a remarkable challenge due to the exponentially increasing number of ways in which quantum systems can share quantum correlations. Tackling such challenge requires a combination of sophisticated theoretical and computational techniques. In this paper we combine machine-learning tools and the theory of quantum entanglement to perform entanglement classification for multipartite qubit systems in pure states. We use a parameterisation of quantum systems using artificial neural networks in a restricted Boltzmann machine architecture, known as Neural Network Quantum States, whose entanglement properties can be deduced via a constrained, reinforcement learning procedure. In this way, Separable Neural Network States can be used to build entanglement witnesses for any target state.

Nowadays, people detection in video surveillance environments is a task that has been generating great interest. There are many approaches trying to solve the problem either in controlled scenarios or in very specific surveillance applications. The main objective of this study is to give a comprehensive and extensive evaluation of the state of the art of people detection regardless of the final surveillance application. For this reason, first, the different processing tasks involved in the automatic people detection in video sequences have been defined, then a proper classification of the state of the art of people detection has been made according to the two most critical tasks, object detection and person model, that are needed in every detection approach. Finally, experiments have been performed on an extensive dataset with different approaches that completely cover the proposed classification and support the conclusions drawn from the state of the art.

Cutting chatter has been a very important issue in the milling operations due to its unexpected and uncontrollable characteristics. Developing an effective healthy condition monitoring method is critical to identify cutting chatter exactly. In this paper, a hybrid healthy condition monitoring (HHCM) method, that combines variational mode decomposition (VMD) with genetic algorithm-based back propagation neural network (BPNN) model, is developed for cutting chatter detection and state classification in complex and non-stationary milling operations. First, cutting chatter vibration signal is decomposed into multiple mode components by the VMD. Then, Shannon power spectral entropy is adopted to extract features from decomposed vibration signals. Furthermore, BPNN model is optimized by traditional genetic algorithm to identify and classify machine states in milling operations. Last, impeller milling experiments are conducted and results show that the proposed HHCM method can effectively realize cutting chatter detection and state classification during milling operations. [ABSTRACT FROM AUTHOR]

Technical systems and system’s components undergo gradual degradation over time. Continuous degradation occurred in system is reflected in decreased system’s reliability and unavoidably lead to a system failure. Therefore, continuous evaluation of State-of-Health (SoH) is inevitable to provide at least predefined lifetime of the system defined by manufacturer, or even better, to extend the lifetime given by manufacturer. However, precondition for lifetime extension is accurate estimation of SoH as well as the estimation and prediction of Remaining Useful Lifetime (RUL). For this purpose, lifetime models describing the relation between system/component degradation and consumed lifetime have to be established. In this contribution modeling and selection of suitable lifetime models from database based on current SoH conditions are discussed. Main contribution of this paper is the development of new modeling strategies capable to describe complex relations between measurable system variables, related system degradation, and RUL. Two approaches with accompanying advantages and disadvantages are introduced and compared. Both approaches are capable to model stochastic aging processes of a system by simultaneous adaption of RUL models to current SoH. The first approach requires a priori knowledge about aging processes in the system and accurate estimation of SoH. An estimation of SoH here is conditioned by tracking actual accumulated damage into the system, so that particular model parameters are defined according to a priori known assumptions about system’s aging. Prediction accuracy in this case is highly dependent on accurate estimation of SoH but includes high number of degrees of freedom. The second approach in this contribution does not require a priori knowledge about system’s aging as particular model parameters are defined in accordance to multi-objective optimization procedure. Prediction accuracy of this model does not highly depend on estimated SoH. This model has lower degrees of freedom. Both approaches rely on previously developed lifetime models each of them corresponding to predefined SoH. Concerning first approach, model selection is aided by state-machine-based algorithm. In the second approach, model selection conditioned by tracking an exceedance of predefined thresholds is concerned. The approach is applied to data generated from tribological systems. By calculating Root Squared Error ( RSE ), Mean Squared Error ( MSE ), and Absolute Error ( ABE ) the accuracy of proposed models/approaches is discussed along with related advantages and disadvantages. Verification of the approach is done using cross-fold validation, exchanging training and test data. It can be stated that the newly introduced approach based on data (denoted as data-based or data-driven) parametric models can be easily established providing detailed information about remaining useful/consumed lifetime valid for systems with constant load but stochastically occurred damage. [ABSTRACT FROM AUTHOR]

Many states use a classificatory approach to foreign policy: they put other states into particular categories and structure their engagement and relations partly as a result. There is one prominent modern international political theory – Rawls’ Law of Peoples – that seems to adopt this approach as an account of justified state behaviour. But should we expect this type of theory ultimately to prove attractive, justified and philosophically distinct compared to more instrumentalist rivals? This paper explores the challenges generic to any such account, not merely those relating to Rawls’ specific version, and surveys possible responses and their shortcomings. [ABSTRACT FROM PUBLISHER]

This paper explores to search for the best classification scheme of autonomic states based on the heart rate variability(HRV). Six drug induced distinct states are artificially created and classified by the multi-layer perceptron(MLP). 14 healthy male subjects aged 19-38 were volunteered to be subject of study. 5minutes heart rate data in different autonomic states are collected for the classification. Optimized binary classification scheme has been utilized and the classification accuracy of sensitivity 0.861 and specificity 0.970 has been achieved. [ABSTRACT FROM AUTHOR]

In this article I examine the ways in which the term 'indigenous peoples' is reworked in a specific South Asian context. I focus on the new, hybrid category of 'indigenous tribe' in the Indian state of Meghalaya. I argue that we can think of the indigenous tribe category as a strategic conflation of two different regimes of rights or political assertions. The first relates to the existing nation-state framework for affirmative action as expressed in the Scheduled Tribe (ST) status, while the second relates to the emerging global framework for asserting the rights of indigenous peoples. While the benefits of asserting the status of indigenous tribes is obvious, for example, preventing other, nonindigenous tribes from owning land in the state, the long-term gains seems more doubtful. Both affirmative action programs and indigenous peoples frameworks are motivated by a moral imperative to redress historical injustices and contemporary social inequalities. To evoke them for other ends might eventually backfire. The larger point I seek to make, however, is that political categories tend to take on a life of their own, escaping their intended purposes and hence applied by people in novel and surprising ways. [ABSTRACT FROM AUTHOR]

State modeling of whole-brain electroencephalography (EEG) or magnetoencephalography (MEG) allows to investigate transient, recurring neurodynamical events. Two widely-used techniques are the microstate analysis of EEG signals and hidden Markov modeling (HMM) of MEG power envelopes. Both reportedly lead to similar state lifetimes on the 100 ms timescale, suggesting a common neural basis. To investigate whether microstates and power envelope HMM states describe the same neural dynamics, we used simultaneous MEG/EEG recordings at rest and compared the spatial signature and temporal activation dynamics of microstates and power envelope HMM states obtained separately from EEG and MEG. Results showed that microstates and power envelope HMM states differ both spatially and temporally. Microstates reflect sharp events of neural synchronization, whereas power envelope HMM states disclose network-level activity with 100–200 ms lifetimes. Further, MEG microstates do not correspond to the canonical EEG microstates but are better interpreted as split HMM states. On the other hand, both MEG and EEG HMM states involve the (de)activation of similar functional networks. Microstate analysis and power envelope HMM thus appear sensitive to neural events occurring over different spatial and temporal scales. As such, they represent complementary approaches to explore the fast, sub-second scale bursting electrophysiological dynamics in spontaneous human brain activity. [ABSTRACT FROM AUTHOR]

In this paper, we decouple the motion-planning problem of humanoid robots into two sub-problems, namely topological state planning and detailed motion planning. The state classification plays a key role for the first sub-problem. We propose several basic states, including lying, sitting, standing and handstanding, abstracted from the daily exercises of human beings. Each basic state is classified further from the topological point of view. Furthermore, generalised function (GF) set theory is applied with the aim of analysing the kinematic characteristics of the end effectors for each state, and meaningful names are assigned for each state. Finally a topological state-planning example is given to show the effectiveness of this methodology. The results show that the large amounts of states can be described using assigned names, which leads to systematic and universal description of the states for humanoid robots. [ABSTRACT FROM AUTHOR]

Research based on medical signals has received significant attention in recent years. If the patients' states can be accurately monitored based on medical signals, it greatly benefits both doctors and patients. This paper proposes a method to extract signal features from heart rate variability signals and classify patients' states using the long short-term memory network and enable effective monitoring of noxious stimulation. For data processing, the heart rate variability signal is decomposed and recombined by the empirical mode decomposition method, and the signal features of the noxious stimulation are extracted by the sliding time window method. Compared with the average accuracy of direct classifications, the classification accuracy based on the proposed method is proved more accurate. The model based on the extracted features proposed can realize the classification of consciousness and general anaesthesia with an accuracy rate of more than 90% and accurately estimate the occurrence of tracheal intubation stimulation. Furthermore, this study shows that combining the deep learning neural network with the extracted more effective signal features under different states and stresses can classify the states with high accuracy. Therefore, it is promising to apply the deep learning method in researching the autonomic nervous system., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

Conscious perception relies on interactions between spatially and functionally distinct modules of the brain at various spatiotemporal scales. These interactions are altered by anesthesia, an intervention that leads to fading consciousness. Relatively little is known about brain functional connectivity and its anesthetic modulation at a fine spatial scale. Here, we used functional imaging to examine propofol-induced changes in functional connectivity in brain networks defined at a fine-grained parcellation based on a combination of anatomical and functional features. Fifteen healthy volunteers underwent resting-state functional imaging in wakeful baseline, mild sedation, deep sedation, and recovery of consciousness. Compared with wakeful baseline, propofol produced widespread, dose-dependent functional connectivity changes that scaled with the extent to which consciousness was altered. The dominant changes in connectivity were associated with the frontal lobes. By examining node pairs that demonstrated a trend of functional connectivity change between wakefulness and deep sedation, quadratic discriminant analysis differentiated the states of consciousness in individual participants more accurately at a fine-grained parcellation (e.g., 2000 nodes) than at a coarse-grained parcellation (e.g., 116 anatomical nodes). Our study suggests that defining brain networks at a high granularity may provide a superior imaging-based distinction of the graded effect of anesthesia on consciousness.