Previous Input Research Articles

MATLAB optimizacija proizvodne jedinice za preduzeće

This research work focused on how to maximize the profit of a mechanical manufacturing firm by optimizing the production units using MATLAB. This numerical analysis was manually done through the Simplex Method to model the production units into a linear mathematical equation that produces four major units which include standard aluminium spoon, bowl, pot and plates. Thereafter, a mathematical model was simulated using MATLAB2013b computational tool based on the previous input data of the mechanical firm and subjected to constraint equations based on the time required to finish each of the three products denoted p, q, r and s. The profit function was subsequently analysed to obtain the quantity of each product that will give maximum profit. The results of the manually calculated linear model corresponded with the MATLAB simulation that revealed that approximately <1 units, 17 units, 10 and 8 units of spoon, bowl, grinding teeth plate and pot respectively will have to be produced per day in order to obtain a maximum daily and monthly profit of #6,120.00 and #134,640.00. Businesses can thus be optimized and made more lucrative when resources and time available are allocated properly.

On low-complexity control design to spacecraft attitude stabilization: An online-learning approach

This paper studies the spacecraft attitude stabilization problem with external disturbances. A new control scheme entitled online-learning control is proposed to achieve a robust, accurate, and simple-structure control algorithm. Compared with the conventional control design, an obvious distinction of the online-learning control algorithm is that it together utilizes the previous control input information and the system's current state information, as if learning experience from previous control input. In contrast, the conventional control scheme does not fully use the existing information and chooses to discard the previous control input information when generating control instructions. Due to the learning strategy, the utility of adaptive- or observer-based tools can be avoided when designing a robust control law, making a simple, effective algorithm, moreover saving system resources. The proposed control law can stabilize the attitude system by achieving the uniformly ultimately bounded convergence.

Efficient thermal simulation of large-scale metal additive manufacturing using hot element addition

Directed energy deposition processes can reduce material waste and manufacturing time of large metal parts through near net-shape production at high deposition rates. However, the localised high heat input gives rise to undesired heat accumulation, residual stresses and distortions. In this work, a fast thermal model is developed to aid in predicting and preventing these drawbacks by providing insight in the relation between process settings, deposition strategy and thermal response. Material addition and heat input are efficiently combined by adding new elements at elevated temperature. Newly deposited elements are assigned an artificially enhanced heat capacity to match the process heat input. The discontinuous Galerkin finite element method is used for spatial discretisation. The resulting numerical scheme is fully explicit and can be solved element-wise. Unlike previous prescribed-temperature heat input models, the proposed method correctly captures the process heat input, irrespective of substrate temperature and element size and without calibration. Comparison with experimental data shows that the thermal history of a large additively manufactured part can be accurately predicted.

Efficient Model-Predictive Control for Nonlinear Systems in Interval Type-2 T-S Fuzzy Form Under Round-Robin Protocol

This article is concerned with the efficient model-predictive control (EMPC) problem for interval type-2 Takagi–Sugeno (IT2 T-S) fuzzy systems with hard constraints. By applying the round-robin (RR) protocol, all controller nodes are activated in a pregiven order such that the occurrence of network congestion or data collisions can be effectively reduced. The aim of the proposed problem is to design a fuzzy controller in the framework of EMPC so as to obtain a good balance among the computation burden, the initial feasible region, and the control performance. With respect to the RR protocol and IT2 T-S fuzzy nonlinearities, a unified representation is modeled for the underlying system, and then, an augmentation state comprising of the system state, the token-dependent perturbation, and the previous input under the RR protocol is put forward; correspondingly, objective functions are constructed for the controller design. Subsequently, by using the min–max strategy, some token-dependent optimizations are established to facilitate the formation of the EMPC algorithm, where the feedback gain is designed offline, and the perturbation is calculated by solving an online optimization dependent of the token. Moreover, with the help of the matrix partition technique, the feasibility of the proposed EMPC algorithm and the stability of the underlying IT2 T-S fuzzy system are rigidly guaranteed. Finally, two numerical examples are utilized to illustrate the validity of the proposed EMPC strategy.

236U, 237Np and 239,240Pu as complementary fingerprints of radioactiveeffluents in the western Mediterranean Sea and in the Canada Basin (Arctic Ocean)

The aim of this study was to assess the potential of combining the conservatively behaving anthropogenic radionuclides 236U and 237Np to gain information on the origin of water masses tagged with liquid effluents from Nuclear Reprocessing Plants. This work includes samples collected from three full-depth water columns in two areas: i) the Arctic Ocean, where Atlantic waters carry the signal of Sellafield (United Kingdom) and La Hague (France) nuclear reprocessing facilities; and ii) the western Mediterranean Sea, directly impacted by Marcoule reprocessing plant (France). This work is complemented by the study of the particle-reactive Pu isotopes as an additional fingerprint of the source region. In the Canada Basin, Atlantic waters showed the highest concentrations and 237Np/236U ratios in agreement with the estimated values for North Atlantic waters entering the Arctic Ocean and tagged with the signal of European Nuclear Reprocessing Plants. These results may reflect the impact of the documented releases for the 1990s. In the Mediterranean Sea, an excess of 236U presumably caused by Marcoule is reflected in the lower 237Np/236U ratios compared to the Global Fallout signal in all the studied samples. On the contrary, the 239,240Pu profiles were mainly governed by the Global Fallout. The impact of Marcoule as a local source is further corroborated when comparing the temporal evolution of these ratios between 2001 and 2013. The lowest 237Np/236U ratios observed in 2001 at the surface reflect a previous local input that is no longer observed in 2013 as it had been homogenized through the whole water column. This work presents the use of 237Np as a new ocean tracer. A more accurate characterization of the main sources is still needed to optimize the use of 236U-237Np as a new tool to understand transient oceanographic processes.

Probabilistic Approach to Physical Object Disentangling

Physically disentangling entangled objects from each other is a problem encountered in waste segregation or in any task that requires disassembly of structures. Often there are no object models, and especially with cluttered irregularly shaped objects, the robot cannot create a model of the scene due to occlusion. One of our key insights is that based on previous sensory input we are only interested in moving an object out of the disentanglement around obstacles. That is, we only need to know where the robot can successfully move in order to plan the disentangling. Due to the uncertainty we integrate information about blocked movements into a probability map. The map defines the probability of the robot successfully moving to a specific configuration. Using as cost the failure probability of a sequence of movements we can then plan and execute disentangling iteratively. Since our approach circumvents only previously encountered obstacles, new movements will yield information about unknown obstacles that block movement until the robot has learned to circumvent all obstacles and disentangling succeeds. In the experiments, we use a special probabilistic version of the Rapidly exploring Random Tree (RRT) algorithm for planning and demonstrate successful disentanglement of objects both in 2-D and 3-D simulation, and, on a KUKA LBR 7-DOF robot. Moreover, our approach outperforms baseline methods.

Memristors Based on 2D Materials as an Artificial Synapse for Neuromorphic Electronics.

The memristor, a composite word of memory and resistor, has become one of the most important electronic components for brain-inspired neuromorphic computing in recent years. This device has the ability to control resistance with multiple states by memorizing the history of previous electrical inputs, enabling it to mimic a biological synapse in the neural network of the human brain. Among many candidates for memristive materials, including metal oxides, organic materials, and low-dimensional nanomaterials, 2D layered materials have been widely investigated owing to their outstanding physical properties and electrical tunability, low-power-switching capability, and hetero-integration compatibility. Hence, a large number of experimental demonstrations on 2D material-based memristors have been reported showing their unique memristive characteristics and novel synaptic functionalities, distinct from traditional bulk-material-based systems. Herein, an overview of the latest advances in the structures, mechanisms, and memristive characteristics of 2D material-based memristors is presented. Additionally, novel strategies to modulate and enhance the synaptic functionalities of 2D-memristor-based artificial synapses are summarized. Finally, as a foreseeing perspective, the potentials and challenges of these emerging materials for future neuromorphic electronics are also discussed.

Experimental implementation of artificial neural network for cost effective and non-intrusive performance estimation of air conditioning systems

Owing to the high variability of operating conditions and the complexity of dynamic phenomena occurring within air conditioning cycles, the realistic performance estimation of these systems remains an open question in this field. This paper demonstrates the applicability of a cost-effective estimation method based on an artificial neural network exclusively using four refrigerant temperatures as the network input. The experimental datasets are collected from a reference experimental facility. The system is operated with variable cooling load, outdoor temperature, and indoor temperature settings, as representative of the actual operation. The artificial neural network structure was optimized by considering the effect of previous time step inputs, number of neurons, sampling time, and number of training data. The results reveal that the developed model can successfully estimate the cooling capacity of an air conditioning system during on–off, continuous unsteady, and steady operation, using four temperature inputs with relative averaged error below 5%.

Short-Term Multi-Vehicle Trajectory Planning for Collision Avoidance

A trajectory planning is a technique that is widely used to automatically generate an expected geometric path that a vehicle needs to follow within a certain future time horizon. We propose a way to generate trajectories of multiple vehicles for a short-term planning with the formal guarantee of the collision avoidance using a SAT (Satisfiability) solver. We assume that each vehicle sends the local status (e.g., speed or position) and its planned trajectory to the server (e.g., road-side units or cloud servers). The server collects the information, and determines if the group of vehicles would encounter any collision within a fixed amount of time. Then, the server calculates the modified trajectories, and sends them back to the vehicles to avoid collisions. Calculating such trajectories is generally a complex problem, which makes it challenging to find them within a short amount of time. Given a driving scenario where multiple vehicles are engaged, we propose two methods to reduce the computation time to generate the trajectories. Firstly, we introduce the grouping method which systematically identifies vehicles that are not engaged in the collision scenario, and removes the related parameters from the SAT solving process. Our case study of the ramp merging driving scenario shows the grouping method saves the computation time by 40% compared to solving the constraints with the original parameters. Secondly, we introduce the collision check point method that chooses some waypoints to maintain their previous control input to reduce the problem complexity. Our experiment shows up to 98% computation time reduction compared to the approach without this method.

W-band system-on-chip electron cyclotron emission imaging system on DIII-D.

Monolithic, millimeter-wave "system-on-chip" (SoC) technology has been employed in heterodyne receiver integrated circuit radiometers in a newly developed Electron Cyclotron Emission Imaging (ECEI) system on the DIII-D tokamak for 2D electron temperature profile and fluctuation evolution diagnostics. A prototype module operating in the E-band (72 GHz-80 GHz) was first employed in a 2 × 10 element array that demonstrated significant improvements over the previous quasi-optical Schottky diode mixer arrays during the 2018 operational campaign of the DIII-D tokamak. For compatibility with International Thermonuclear Experimental Reactor relevant scenarios on DIII-D, the SoC ECEI system was upgraded with 20 horn-waveguide receiver modules. Each individual module contains a University of California Davis designed W-band (75 GHz-110 GHz) receiver die that integrates a broadband low noise amplifier, a double balanced down-converting mixer, and a ×4 multiplier on the local oscillator (LO) chain. A ×2 multiplier and two IF amplifiers are packaged and selected to further boost the signal strength and downconvert the signal frequency. The upgraded W-band array exhibits >30 dB additional gain and 20× improvement in noise temperature compared with the previous Schottky diode radio frequency mixer input systems; an internal 8 times multiplier chain is used to bring down the LO frequency below 12 GHz, thereby obviating the need for a large aperture for quasi-optical LO coupling and replacing it with coaxial connectors. Horn-waveguide shielding housing avoids out-of-band noise interference on each individual module. The upgraded ECEI system plays an important role for absolute electron temperature evolution and fluctuation measurements for edge and core region transport physics studies.

Concepts for E-Assessments in STEM on the Example of Engineering Mechanics

We discuss if and how it is possible to develop meaningful e-assessments in Engineering Mechanics. The focus is on complex example problems, resembling traditional paper-pencil exams. Moreover, the switch to e-assessments should be as transparent as possible for the students, i.e., it shouldn’t lead to additional difficulties, while still maintaining sufficiently high discrimination indices for all questions. Example problems have been designed in such a way, that it is possible to account for a great variety of inputs ranging from graphical to numerical and algebraic as well as string input types. Thanks to the implementation of random variables it is even possible to create an individual set of initial values for every participant. Additionally, when dealing with complex example problems errors carried forward have to be taken into account. Different approaches to do so are detailed and discussed, e.g., pre-defined paths for sub-questions, usage of students’ previous inputs or decision trees. The main finding is that complex example problems in Engineering Mechanics can very well be used in e-assessments if the design of these questions is well structured into meaningful sub-questions and errors carried forward are accounted for.

Efficient Model-Predictive Control for Networked Interval Type-2 T–S Fuzzy System With Stochastic Communication Protocol

In this article, the efficient model-predictive control (EMPC) problem of a class of nonlinear systems in the framework of interval type-2 Takagi-Sugeno (IT2 T-S) fuzzy is investigated. In order to improve the reliability of the data transmission while reducing the network communication burden, a so-called stochastic communication protocol (SCP) governed by a Markov chain is adopted to orchestrate the data transmission order from the controller to the actuator. The purpose of the addressed problem is to design a set of desired EMPC controllers so as to guarantee the mean-square system stability and obtain a good balance among the computation burden, initial feasible region, and the control performance. A novel control model is established for the SCP and IT2 T-S fuzzy nonlinearities in a unified representation by using a fuzzy periodic switching related to the transmission token and the membership function. Then, the system state, the SCP-based control perturbation, and the previous input under the SCP are fully taken into consideration for constructing the objective function. By virtue of the “min-max” strategy, a few optimizations are formulated, and the corresponding EMPC algorithm is provided, where the feedback gain is designed offline, while the control perturbation is obtained online. Furthermore, by means of the matrix partition technique, sufficient conditions are presented to rigidly guarantee the feasibility of the proposed EMPC algorithm and the mean-square stability of the underlying IT2 T-S fuzzy system. Finally, two illustrative examples are utilized to demonstrate the validity of the proposed EMPC strategy.

On the Potential of Time Delay Neural Networks to Detect Indirect Coupling between Time Series

Determining the coupling between systems remains a topic of active research in the field of complex science. Identifying the proper causal influences in time series can already be very challenging in the trivariate case, particularly when the interactions are non-linear. In this paper, the coupling between three Lorenz systems is investigated with the help of specifically designed artificial neural networks, called time delay neural networks (TDNNs). TDNNs can learn from their previous inputs and are therefore well suited to extract the causal relationship between time series. The performances of the TDNNs tested have always been very positive, showing an excellent capability to identify the correct causal relationships in absence of significant noise. The first tests on the time localization of the mutual influences and the effects of Gaussian noise have also provided very encouraging results. Even if further assessments are necessary, the networks of the proposed architecture have the potential to be a good complement to the other techniques available in the market for the investigation of mutual influences between time series.

Optimizing perception: Attended and ignored stimuli create opposing perceptual biases.

Humans have remarkable abilities to construct a stable visual world from continuously changing input. There is increasing evidence that momentary visual input blends with previous input to preserve perceptual continuity. Most studies have shown that such influences can be traced to characteristics of the attended object at a given moment. Little is known about the role of ignored stimuli in creating this continuity. This is important since while some input is selected for processing, other input must be actively ignored for efficient selection of the task-relevant stimuli. We asked whether attended targets and actively ignored distractor stimuli in an odd-one-out search task would bias observers' perception differently. Our observers searched for an oddly oriented line among distractors and were occasionally asked to report the orientation of the last visual search target they saw in an adjustment task. Our results show that at least two opposite biases from past stimuli influence current perception: A positive bias caused by serial dependence pulls perception of the target toward the previous target features, while a negative bias induced by the to-be-ignored distractor features pushes perception of the target away from the distractor distribution. Our results suggest that to-be-ignored items produce a perceptual bias that acts in parallel with other biases induced by attended items to optimize perception. Our results are the first to demonstrate how actively ignored information facilitates continuity in visual perception.

Bivariate Beta-LSTM

Long Short-Term Memory (LSTM) infers the long term dependency through a cell state maintained by the input and the forget gate structures, which models a gate output as a value in [0,1] through a sigmoid function. However, due to the graduality of the sigmoid function, the sigmoid gate is not flexible in representing multi-modality or skewness. Besides, the previous models lack modeling on the correlation between the gates, which would be a new method to adopt inductive bias for a relationship between previous and current input. This paper proposes a new gate structure with the bivariate Beta distribution. The proposed gate structure enables probabilistic modeling on the gates within the LSTM cell so that the modelers can customize the cell state flow with priors and distributions. Moreover, we theoretically show the higher upper bound of the gradient compared to the sigmoid function, and we empirically observed that the bivariate Beta distribution gate structure provides higher gradient values in training. We demonstrate the effectiveness of the bivariate Beta gate structure on the sentence classification, image classification, polyphonic music modeling, and image caption generation.

Finite-dimensional control of linear discrete-time fractional-order systems

This paper addresses the design of finite-dimensional feedback control laws for linear discrete-time fractional-order systems with additive state disturbance. A set of sufficient conditions are provided to guarantee convergence of the state trajectories to an ultimate bound around the origin with size increasing with the magnitude of the disturbances. Performing a suitable change of coordinates, the latter result can be used to design a controller that is able to track reference trajectories that are solutions of the unperturbed fractional-order system. To overcome the challenges associated with the generation of such solutions, we address the practical case where the references to be tracked are generated as a solution of a specific finite-dimensional approximation of the original fractional-order system. In this case, the tracking error trajectory is driven to an asymptotic bound that is increasing with the magnitude of the disturbances and decreases with the increment in the accuracy of the approximation. The proposed controllers are finite-dimensional, in the sense that the computation of the control input only requires a finite number of previous state and input vectors of the system. Numerical simulations illustrate the proposed design methods in different scenarios.

Improved Iterative Learning Control Strategy for Surface-Mounted Permanent Magnet Synchronous Motor Drives

In this article, an improved iterative learning control (ILC), which significantly enhances the speed tracking performance in the transient and steady state for a surface-mounted permanent magnet synchronous motor (SPMSM) drive, is proposed. The proposed ILC encapsulates two control terms: the feedback linearization control terms force the speed error to approach zero, and the iterative learning control terms (ILCTs) improve the performance of the control inputs based on the stored data such as previous speed error and previous control input. Unlike the conventional feedback linearization control (FLC), the proposed ILC does not require the exact information of the SPMSM parameters, significantly rejects the periodic and nonperiodic disturbances, and efficiently provides an enhanced speed tracking performance owing to the included ILCTs. Besides, its stability is proven by showing that the speed tracking error asymptotically goes to zero. In comparative studies, the proposed method offers the better transient performance (e.g., faster transient response and smaller overshoot) and steady-state performance (i.e., smaller steady-state error) than the conventional FLC under load and speed step changes. To prove the practicability of the proposed scheme, the proposed ILC is simulated and implemented on a MATLAB/Simulink software and a prototype SPMSM test-bed using TI TMS320F28335 digital signal processor, respectively.

A Feasible Dashboard to predict Patent Mining Using Classification Algorithms

The number of patents that are being filed across the world is increasing day by day. With the increase in patents being filed the process of segregating the patents based on their class becomes even more difficult. There are a set of features that are extracted from the dataset that is previously present. The features that are being extracted will vary for each document. Patents documents are issued as legal rights by the government for protecting the owner of invention. This exclusive right helps in protecting the invention to be used by others from using, selling, developing another, etc for some period of time. After the feature extraction is done there are two steps that need to be carried out, namely: Classification and prediction. For this purpose, decision tree algorithm is used which makes use of the most prominent feature and classification is done using those features. Therefore, for classification a hierarchical decision tree algorithm is used along with the probability of patent conversion. Based on the classification that is done a model will be created and whenever a new entity is brought it is compared with the model file that was created using the available datasets and is predicted as a particular class. Thus, both classification of existing dataset and the prediction for any new dataset based on previous inputs can be achieved thereby facilitating the patent mining process In proposed system this paper mainly concentrating on the prediction of the patent using data mining techniques. Decision tree algorithm for the classification of the patent mining is applied. And also classification of the data using some attributes is done, namely; association frequency, grant or applicant, patent country, patent status, grant patent using these attributes prediction of patent mining using Naive Bayes algorithm is done. The proposed algorithm gives best result for patent mining. The patent status is checked whether he got grant access of the patent in prediction which is the best patent mining in all over the country. The graph is plotted for to find the accuracy. Based on this if the new tuple is add to dataset it has to classify in which patent domain it belongs to.

Enhance Medical Sentiment Vectors through Document Embedding using Recurrent Neural Network

Adverse Drug Reaction (ADR) extraction is the process of identifying drug implications mentioned in social posts. Handling medical text for the identification of ADR is vital to research in terms of configuring the side effect and other medical-related entities within any medical text. However, investigating the role of such effect in the context of positive and negative is the responsibility of sentiment classification task where every medical review document would be categorized into its polarity, this is known as Medical Sentiment Analysis (MSA). Several studies have presented various techniques for MSA. Most of the recent studies have concentrated on architectures such as the Convolutional Neural Network (CNN) to get the document embedding. Yet, such architecture focuses only on the input without considering the previous or latter input. This might lead to weaker embedding for the document where some terms would not be considered. Hence, this paper proposes a new document embedding approach based on the Recurrent Neural Network (RNN) to improve the sentiment classification. Using a benchmark dataset of medical sentiments, the proposed method showed greater performance of sentiment classification accuracy. Such finding proves the effectiveness of RNN in producing document embedding.

Delay-Time Adjustable Input Shaping Method for Positioning Systems Subject to Repetitive Motion

Most positioning systems experience residual vibration during operation. Such residual vibration can be eliminated or reduced to an acceptable level by using the input shaping method. However, adopting the input shaping methods typically introduces a certain amount of time-delay into a system. This study focused on the development of a delay-time adjustable input shaping method to eliminate vibration caused by repetitive motion in positioning systems. The proposed input shaping method, called the virtual mode (VM) input shaper, uses a virtual frequency parameter that adjusts delay-time and cancels residual vibration. Unlike most previous input shaping studies, this study investigated VM input shaping performance to eliminate the steady-state vibration induced by repetitive motion in positioning systems. To this end, an analytical formulation was derived and used for simulating the input shaping performance with varying dominant parameters involved in a system. Experiments were also performed to validate the proposed method.