Journal articles on the topic 'Asy tested finite state machine'

We introduce some new models of infinite-state transition systems. The basic model, called a (reversal-bounded) counter machine (CM), is a nondeterministic finite automaton augmented with finitely many reversal-bounded counters (i.e. each counter can be incremented or decremented by 1 and tested for zero, but the number of times it can change mode from nondecreasing to nonincreasing and vice-versa is bounded by a constant, independent of the computation). We extend a CM by augmenting it with some familiar data structures: (i) A pushdown counter machine (PCM) is a CM augmented with an unrestricted pushdown stack. (ii) A tape counter machine (TCM) is a CM augmented with a two-way read/write worktape that is restricted in that the number of times the head crosses the boundary between any two adjacent cells of the worktape is bounded by a constant, independent of the computation (thus, the worktape is finite-crossing). There is no bound on how long the head can remain on a cell. (iii) A queue counter machine (QCM) is a CM augmented with a queue that is restricted in that the number of alternations between non-deletion phase and non-insertion phase on the queue is bounded by a constant. A non-deletion (non-insertion) phase is a period consisting of insertions (deletions) and no-changes, i.e., the queue is idle. We show that emptiness, (binary, forward, and backward) reachability, nonsafety, and invariance for these machines are solvable. We also look at extensions of the models that allow the use of linear-relation tests among the counters and parameterized constants as "primitive" predicates. We investigate the conditions under which these problems are still solvable.

This paper presents a semiautomatic technique for developing broad-coverage finite-state morphological analyzers for use in natural language processing applications. It consists of three components—elicitation of linguistic information from humans, a machine learning bootstrapping scheme, and a testing environment. The three components are applied iteratively until a threshold of output quality is attained. The initial application of this technique is for the morphology of low-density languages in the context of the Expedition project at NMSU Computing Research Laboratory. This elicit-build-test technique compiles lexical and inØectional information elicited from a human into a finite-state transducer lexicon and combines this with a sequence of morphographemic rewrite rules that is induced using transformation-based learning from the elicited examples. The resulting morphological analyzer is then tested against a test set, and any corrections are fed back into the learning procedure, which then builds an improved analyzer.

A main research direction in the field of evolutionary machine learning is to develop a scalable classifier system to solve high-dimensional problems. Recently work has begun on autonomously reusing learned building blocks of knowledge to scale from low-dimensional problems to high-dimensional ones. An XCS-based classifier system, known as XCSCFC, has been shown to be scalable, through the addition of expression tree–like code fragments, to a limit beyond standard learning classifier systems. XCSCFC is especially beneficial if the target problem can be divided into a hierarchy of subproblems and each of them is solvable in a bottom-up fashion. However, if the hierarchy of subproblems is too deep, then XCSCFC becomes impractical because of the needed computational time and thus eventually hits a limit in problem size. A limitation in this technique is the lack of a cyclic representation, which is inherent in finite state machines (FSMs). However, the evolution of FSMs is a hard task owing to the combinatorially large number of possible states, connections, and interaction. Usually this requires supervised learning to minimize inappropriate FSMs, which for high-dimensional problems necessitates subsampling or incremental testing. To avoid these constraints, this work introduces a state-machine-based encoding scheme into XCS for the first time, termed XCSSMA. The proposed system has been tested on six complex Boolean problem domains: multiplexer, majority-on, carry, even-parity, count ones, and digital design verification problems. The proposed approach outperforms XCSCFA (an XCS that computes actions) and XCSF (an XCS that computes predictions) in three of the six problem domains, while the performance in others is similar. In addition, XCSSMA evolved, for the first time, compact and human readable general classifiers (i.e., solving any n-bit problems) for the even-parity and carry problem domains, demonstrating its ability to produce scalable solutions using a cyclic representation.

Modeling of soft robotics systems proves to be an extremely difficult task, due to the large deformation of the soft materials used to make such robots. Reliable and accurate models are necessary for the control task of these soft robots. In this paper, a data-driven approach using machine learning is presented to model the kinematics of Soft Pneumatic Actuators (SPAs). An Echo State Network (ESN) architecture is used to predict the SPA’s tip position in 3 axes. Initially, data from actual 3D printed SPAs is obtained to build a training dataset for the network. Irregular-intervals pressure inputs are used to drive the SPA in different actuation sequences. The network is then iteratively trained and optimized. The demonstrated method is shown to successfully model the complex non-linear behavior of the SPA, using only the control input without any feedback sensory data as additional input to the network. In addition, the ability of the network to estimate the kinematics of SPAs with different orientation angles θ is achieved. The ESN is compared to a Long Short-Term Memory (LSTM) network that is trained on the interpolated experimental data. Both networks are then tested on Finite Element Analysis (FEA) data for other θ angle SPAs not included in the training data. This methodology could offer a general approach to modeling SPAs with varying design parameters.

A finite element method (FEM) - boundary element method (BEM) model is developed to compute the sound generated by of a force acting on a circular membrane (drumhead). A vibro-acoustic analysis that combines modal FEM analysis, a FEM steady state dynamic analysis (SSD), considering harmonic loading and boundary element acoustics, is performed. The drumhead vibrates due to the force impact and the sound is emitted in the air. The vibration of structural response is initially computed, and the obtained results are set to be the boundary conditions of the acoustic analysis in the vibro-acoustic simulation. The radiated sound can be computed at any point of the solution domain. Various materials used by drumhead manufacturers are tested and a parametric analysis focusing on the mesh density of the models is presented. The impact sound and the acoustical characteristics of the simulated test cases are evaluated. The Rayleigh method is also applied to the acoustic simulations and is further compared to the BEM simulation results. The outcomes of this study may be further used as reverse engineering inputs, to machine learning models for the estimation of the physical and mechanical parameters of drumheads from audio signals.

Human activity recognition and modelling comprise an area of research interest that has been tackled by many researchers. The application of different machine learning techniques including regression analysis, deep learning neural networks, and fuzzy rule-based models has already been investigated. In this paper, a novel method based on Fuzzy Finite State Machine (FFSM) integrated with the learning capabilities of Neural Networks (NNs) is proposed to represent human activities in an intelligent environment. The proposed approach, called Neuro-Fuzzy Finite State Machine (N-FFSM), is able to learn the parameters of a rule-based fuzzy system, which processes the numerical input/output data gathered from the sensors and/or human experts’ knowledge. Generating fuzzy rules that represent the transition between states leads to assigning a degree of transition from one state to another. Experimental results are presented to demonstrate the effectiveness of the proposed method. The model is tested and evaluated using a dataset collected from a real home environment. The results show the effectiveness of using this method for modelling the activities of daily living based on ambient sensory datasets. The performance of the proposed method is compared with the standard NNs and FFSM techniques.

A video-based fall detection system was presented; which consists of data acquisition, image processing, feature extraction, feature selection, classification and finite state machine. A two-dimensional human posture image was represented by 12 features extracted from the generalisation of a silhouette shape to a quadrilateral. The corresponding feature vectors for three groups of human pose were statistically analysed by using a non-parametric Kruskal Wallis test to assess the different significance level between them. From the statistical test, non-significant features were discarded. Four selected kernel-based Support Vector Machine: linear, quadratics, cubic and Radial Basis Function classifiers were trained to classify three human posture groups. Among four classifiers, the last one performed the best in terms of performance matric on testing set. The classifier outperformed others with high achievement ofaverage sensitivity, precision and F-score of 99.19%, 99.25% and 99.22%, respectively. Such pose classification model output was further used in a simple finite state machine to trigger the falling event alarms. The fall detection system was tested on different fall video sets and able to detect the presence offalling events in a frame sequence of videos with accuracy of 97.32% and low computional time.

At this time, many gases are at risk in the surrounding area, especially in industrial areas, such as radiological materials and toxic gases that can contaminate the surrounding area as well as the occurrence of gas leaks. From the problem-created gas detection simulation robot using Finite State Machine (FSM) model, the robot can help human work in this case the robot helps detect gas leaks that if faced by humans will be dangerous. The robot was created to detect nearby gases and provide notifications in the event of a gas leak. The robot was tested in several areas to see if the robot could perform the task according to the input given, the trial was conducted 40 times with 4 different arenas, from 40 tests the robot can perform the correct task 37 times and error 3 times so that accuracy results obtained by 92.5%. The creation of this gas detection simulation robot is expected to be developed again as a gas leak detection inspection robot as an early warning system.

AbstractA challenging key aspect of modelling and recognising human activity is to design a model that can deal with the uncertainty in human behaviour. Several machine learning and deep learning techniques are employed to model the Activity of Daily Living (ADL) representing the human activity. This paper proposes an enhanced Fuzzy Finite State Machine (FFSM) model by combining the classical FFSM with Long Short-Term Memory (LSTM) neural network and Convolutional Neural Network (CNN). The learning capability in the LSTM and CNN allows the system to learn the relationship in the temporal human activity data and to identify the parameters of the rule-based system as building blocks of the FFSM through time steps in the learning mode. The learned parameters are then used for generating the fuzzy rules that govern the transitions between the system’s states representing activities. The proposed enhanced FFSMs were tested and evaluated using two different datasets; a real dataset collected by our research group and a public dataset collected from CASAS smart home project. Using LSTM-FFSM, the experimental results achieved $$95.7\%$$ 95.7 % and $$97.6\%$$ 97.6 % for the first dataset and the second dataset, respectively. Once CNN-FFSM was applied to both datasets, the obtained results were $$94.2\%$$ 94.2 % and $$99.3\%$$ 99.3 % , respectively.

The optimization methods of the logic circuit of Moore finite-state-machine are proposed. These methods are based on the existence of pseudo equivalent states of Moore finite-state-machine, wide fan-in of PAL macrocells, and free resources of embedded memory blocks. The methods are oriented on hypothetical VLSI microcircuits based on CPLD technology and containing PAL macrocells and embedded memory blocks. The conditions of effective application of each proposed method are shown. An algorithm of choice of the best model of finite-state-machine for given conditions is proposed. The examples of proposed methods application are given. The effectiveness of the proposed methods is also investigated. The analysis of the effectiveness of proposed methods showed that optimal in the given conditions method always permits a decrease of the hardware amount in comparison with earlier known methods of the Moore finite-state-machine design. This decrease in hardware does not lead to a decrease in the performance of the control unit. Moreover, there are some special cases, when some other models of Moore finite-state-machine are more effective. The proposed methods can be modified for real CPLDs, where embedded memory blocks are absent. In this case, the system of microoperations is implemented using PAL macrocells too. The same effectiveness of proposed methods should be tested for both cases of FPGA with embedded memory blocks and for CPLD CoolRunner based on PLA technology. The proposed methods should be modified to meet the specific requirements of these chips.

CNN is an acronym for either Cellular Neural Network when used in the context of brain science, or Cellular Nonlinear Network when used in the context of coupled dynamical systems. A CNN is defined by two mathematical constructs: 1. A spatially discrete collection of continuous nonlinear dynamical systems called cells, where information can be encrypted into each cell via three independent variables called input, threshold, and initial state. 2. A coupling law relating one or more relevant variables of each cell Cij to all neighbor cells Ckl located within a prescribed sphere of influence Sij(r) of radius r, centered at Cij. In the special case where the CNN consists of a homogeneous array, and where its cells have no inputs, no thresholds, and no outputs, and where the sphere of influence extends only to the nearest neighbors (i.e. r = 1), the CNN reduces to the familiar concept of a nonlinear lattice. The bulk of this three-part exposition is devoted to the standard CNN equation [Formula: see text] where xij, yij, uij and zij are scalars called state, output, input, and threshold of cell Cij; akl and bkl are scalars called synaptic weights, and Sij(r) is the sphere of influence of radius r. In the special case where r = 1, a standard CNN is uniquely defined by a string of "19" real numbers (a uniform thresholdzkl = z, nine feedback synaptic weights akl, and nine control synaptic weights bkl) called a CNN gene because it completely determines the properties of the CNN. The universe of all CNN genes is called the CNN genome. Many applications from image processing, pattern recognition, and brain science can be easily implemented by a CNN "program" defined by a string of CNN genes called a CNN chromosome. The first new result presented in this exposition asserts that every Boolean function of the neighboring-cell inputs can be explicitly synthesized by a CNN chromosome. This general theorem implies that every cellular automata (with binary states) is a CNN chromosome. In particular, a constructive proof is given which shows that the game-of-life cellular automata can be realized by a CNN chromosome made of only three CNN genes. Consequently, this "game-of-life" CNN chromosome is a universal Turing machine, and is capable of self-replication in the Von Neumann sense [Berlekamp et al., 1982]. One of the new concepts presented in this exposition is that of a generalized cellular automata (GCA), which is outside the framework of classic cellular (Von Neumann) automata because it cannot be defined by local rules: It is simply defined by iterating a CNN gene, or chromosome, in a "CNN DO LOOP". This new class of generalized cellular automata includes not only global Boolean maps, but also continuum-state cellular automata where the initial state configuration and its iterates are real numbers, not just a finite number of states as in classical (von Neumann) cellular automata. Another new result reported in this exposition is the successful implementation of an analog input analog output CNN universal machine, called a CNN universal chip, on a single silicon chip. This chip is a complete dynamic array stored-program computer where a CNN chromosome (i.e. a CNN algorithm or flow chart) can be programmed and executed on the chip at an extremely high speed of 1 Tera (1012) analog instructions per second (based on a 100 × 100 chip). The CNN universal chip is based entirely on nonlinear dynamics and therefore differs from a digital computer in its fundamental operating principles. Part II of this exposition is devoted to the important subclass of autonomous CNNs where the cells have no inputs. This class of CNNs can exhibit a great variety of complex phenomena, including pattern formation, Turing patterns, knots, auto waves, spiral waves, scroll waves, and spatiotemporal chaos. It provides a unified paradigm for complexity, as well as an alternative paradigm for simulating nonlinear partial differential equations (PDE's). In this context, rather than regarding the autonomous CNN as an approximation of nonlinear PDE's, we advocate the more provocative point of view that nonlinear PDE's are merely idealizations of CNNs, because while nonlinear PDE's can be regarded as a limiting form of autonomous CNNs, only a small class of CNNs has a limiting PDE representation. Part III of this exposition is rather short but no less significant. It contains in fact the potentially most important original results of this exposition. In particular, it asserts that all of the phenomena described in the complexity literature under various names and headings (e.g. synergetics, dissipative structures, self-organization, cooperative and competitive phenomena, far-from-thermodynamic equilibrium phenomena, edge of chaos, etc.) are merely qualitative manifestations of a more fundamental and quantitative principle called the local activity dogma. It is quantitative in the sense that it not only has a precise definition but can also be explicitly tested by computing whether a certain explicitly defined expression derived from the CNN paradigm can assume a negative value or not. Stated in words, the local activity dogma asserts that in order for a system or model to exhibit any form of complexity, such as those cited above, the associated CNN parameters must be chosen so that either the cells or their couplings are locally active.

Here we present a new method for the decomposition of a Finite State Machine (FSM) into a network of interacting FSMs and a framework for the functional verification of the FSM network at different levels of abstraction. The problem of decomposition is solved by output partitioning and state space decomposition using a multiway graph partitioning technique. The number of submachines is determined dynamically during the partitioning process. The verification algorithm can be used to verify (a) the result of FSM decomposition on a behavioral level, (b) the encoded FSM network, and (c) the FSM network after logic optimization. Our verification technique is based on an efficient enumeration-simulation method which involves traversal of the state transition graph of the prototype machine and simulation of the decomposed machine network. Both the decomposition and verification/simulation algorithms have been implemented as part of an interactive FSM synthesis system and tested on a set of benchmark examples.

Abstract In this paper, a new constitutive matrix for thermal conduction in transient thermal regime is developed and tested. We use cell method as a numerical method that is included in finite formulation methodology. The constitutive matrix defines through the cell method the behavior of solids when they are under a thermal potential. We have demonstrated that this matrix is equivalent to the electrical conduction constitutive matrix in steady state. We have applied this constitutive matrix to thermal analysis of asynchronous electric machines in transient regime. This constitutive matrix has been validated with comparisons based on finite element method. In finite formulation, the physical laws governing the electromagnetic fields and the physical thermal phenomena are expressed in integral formulation. The final algebraic equation system is tailored directly without discretizing of the differential equations. This is an important advantage because we omit a complex differential formulation and the discretization of the respective equations.

This paper presents the evolution of a robotic architecture intended for controlling autonomous social robots. The first instance of this architecture was originally designed according to behavior-based principles. The building blocks of this architecture were behaviors designed as a finite state machine and organized in an ethological inspired way. However, the need of managing explicit symbolic knowledge in human–robot interaction required the integration of planning capabilities into the architecture and a symbolic representation of the environment and the internal state of the robot. A major contribution of this paper is the description of the working memory that integrates these two approaches. This working memory has been implemented as a distributed graph. Another contribution is the use of behavior trees instead of state machine for implementing the behavior-based part of the architecture. This late version of the architecture has been tested in robotic competitions (RoboCup or European Robotics League, among others), whose performance is also discussed in this paper.

Phonological patterns have been characterized as regular, and regular patterns are those that are accepted by a finite state machine. However, being regular is only a necessary condition, but not a sufficient condition for phonology. Two subregular classes which further restrict the computational properties of phonological patterns have been identified: strictly piecewise (SP), and strictly local (SL). In this study, the learnability of a SP pattern and a pattern from the regular class (but not SP/SL) were tested by using the artificial language learning paradigm, and the results suggest that the identified computational boundaries are psychologically real.

In this paper, we present the results of experimental tests and numerical calculations for parts of foundry mold devices made by selective laser melting (SLM). The main aim of this research was to compare the heat conduction efficiency of the conformal and the traditional channel arrangement. The infusion spreader with a conformal channel arrangement and the test material were made with an M2 Concept Laser Cusing machine using 1.2709 steel powder. Temperature changes in the spreaders were compared between conventional and conformal cooling channels using finite element method (FEM) calculations. The position of the so-called “thermal equilibrium isotherm” was determined for both sprue spreaders, which separate the area of the mold with a constant temperature from the zone of cyclic temperature changes. The components of the sprue spreaders in a stress state caused by temperature changes during the operation of the pressure machine were determined using the FEM model. It was found that the cooling system shortened the time of solidification and cooling of the alloy. Based on the analysis of the strength test results and the fracture surface of the samples, the relationship between heat treatment parameters and the strength, hardness, and elongation of the tested material was determined. The sprue spreaders were installed under a pressure machine and tested under production conditions. The use of a sprue spreader with a conformal cooling system shortened the time of a single cycle of the casting machine compared to the conventional solution.

Fighters’ air strikes play a vital role in modern warfare. But, with the development of fighters, the amount of bombs will inevitably become a major disadvantage due to the limitations of the invisible design and other functions, so a combination of the old model bombers and the advanced fifth-generation fighters has been proposed. This paper takes the aircraft missile horizontal backward launch system as the research object; firstly, the finite element model of the missile’s off-track is established, and the simulation calculation under the fixed platform is tested to verify the correctness of the finite element model. Meanwhile, the simulation parameters of the initial trajectory are obtained. Then, by establishing the separation model of the machine under the open state of the aircraft’s rear launcher, the variation of the flow field during the separation process of the missile is analyzed, and the variation of the force and the attitude of the missile is studied. It is found that the pitching motion of the missile is greatly affected by the initial pitch angle, it is always in the heading state during the whole separation process, and the yaw motion is not obvious.

Abstract Porous graphite is employed for the development of air bearings used in the ultraprecision machine tools. The static performance of this new bearing type depends on the permeability and inertia coefficient of the porous graphite inserted in it. In this study, an experimental fitting method was used in conjunction with computational fluid dynamics (CFD) modeling to determine the permeability and inertia coefficient of three types of porous graphite with porosity of 16%, 13%, and 8% respectively. The experimental results show that the Compressible-Darcy-Forchheimer equation can fit the experimental mass flow rate and pressure drop well. The average permeability of SG-3, SG-5 and SG-8 porous graphite are 5.74 × 10 − 14 m2, 3.65 × 10 − 15 m2 and 1.85 × 10 − 15 m2 respectively. SG-5 and SG-8 porous graphite have good permeability consistency and can be used to make porous media air bearing. The PPSM (number of pores per square millimeter) of SG-3 and SG-8 are similar, but the permeabilities are very different. For low permeability porous graphite, samples with different sizes from the same material should be tested and the averaged inertia coefficient can be used. The accuracy of the pore-scale FVM (finite volume method) is highly dependent on the quality of the pore microstructure image.

The stability of Border gateway protocol (BGP) are directly related to the performance of the Internet. However, experiments show that BGP has serious slow convergence problem and its performances cannot meet communication needs well. Thus, a BGP fast convergence mechanism based on doubtful path judgment is proposed in this paper. Firstly, by analyzing the relationships between failure paths, doubtful paths containing failure links are computed. Secondly, a policy of doubtful path validity inquiry and its corresponding messages are designed to avoid computing wrong doubtful paths while multiple links fail simultaneously. Meanwhile, in order to avoid the waste of network resources caused by the policy, a finite state machine is designed to handle nodes’ state transitions. Finally, doubtful paths are suppressed to take part in routing decisions in the stage of BGP path exploration and the fast convergence is achieved. The mechanism is implemented on the prototype system, and its validity and practicability are tested. Experiment results show that the mechanism is feasible and effective.

A novel winding type permanent magnet coupling (WTPMC) is proposed to work as an adjustable speed drive with slip power recovery function. As a kind of dual-mechanical-port electric machine with radial-flux configuration, the WTPMC consists of an outer rotor embedded with three-phase windings, an inner rotor populated with permanent magnets, and a slip power recovery circuit comprising a rectifier, a boost converter, and an ultracapacitor. The working principle of the WTPMC is presented, and its mathematical model is derived. To develop a WTPMC prototype for automotive applications, two-dimensional (2D) finite element analysis (FEA) is conducted using Ansoft Maxwell software to study the steady-state (constant slip speed) performance. For the experimental validation, the WTPMC prototype is manufactured and tested on a test bench. To show the accuracy of the 2D FEA, the computed results are compared with those obtained from experimental measurements. It is shown that the agreement between the 2D FEA and experimental results is good. Moreover, the WTPMC prototype can operate in the output speed range under different load torque conditions. The slip power recovery efficiency for the 2D FEA is 66.7%, while, for experimental measurements, it is 57.2%.

A mesh partitioning strategy is presented which facilitates the application of boundary conditions to irregular shaped cooling channels in the pressure diecasting process. The strategy is used to partition a boundary element mesh, but can also be applied to the surface of a cooling channel bounded by a finite element mesh. The partitioning of the mesh into a series of element packs enables a one-dimensional flow model to be applied to the coolant. The flow model is used in conjunction with a steady-state thermal model which initially assumes that no boiling is taking place on the die/coolant interface. Values of bulk temperature, pressure, and velocity in the coolant are thus ascertained. This information, together with die temperatures, is then used in empirical relationships which model the various heat transfer mechanisms, including nucleate and transitional film boiling, between die and coolant. Effective heat transfer coefficients are calculated and applied at the die/coolant interface. The steady-state thermal code and the empirical boiling model are then used iteratively until stable values for the effective heat transfer coefficients are obtained. The models are tested by casting a small thin component using a die with conventional cooling channels and also using a novel die with irregular shaped cooling channels running on a hot chamber proprietary die casting machine. Simulation results are shown and experimental results using the hot chamber pressure die casting machine are reported. [S1087-1357(00)02302-9]

Use case analysis is a common technique used to ascertain the functional requirements of a software system. A use case diagram is a kind of Unified Modeling Language (UML) diagram created for use case analysis. Creating effective use cases can be a determining factor in building a system that meets users' needs. However, writing use cases is a difficult and time-consuming process, requiring the user to manually fill out a form or write text in a specific, pre-stipulated format. Many students lack the technical knowhow to do this. Our research offers a software solution that resolves this issue. By combining natural language algorithms, such as Part Of Speech (POS) and Name Entity recognition (NE), with a set of grammatical rules created and implemented as a Finite State Machine (FSM), our system extracts the relevant items from the text and automatically translates the plain or unstructured text into a structured one. The paper has been tested on standard examples with excellent results.

Insulated Gate Bipolar Transistor (IGBT) is a high-power switch in the field of power electronics. Its reliability is closely related to system stability. Once failure occurs, it may cause irreparable loss. Therefore, potential fault diagnosis methods of IGBT devices are studied in this paper, and their classification accuracy is tested. Due to the shortcomings of incomplete data application in the traditional method of characterizing the device state based on point frequency parameters, a fault diagnosis method based on full frequency threshold screening was proposed in this paper, and its classification accuracy was detected by the Extreme Learning Machine (ELM) algorithm. The randomly generated input layer weight ω and hidden layer deviation lead to the change of output layer weight β and then affect the overall output result. In view of the errors and instability caused by this randomness, an improved Finite Impulse Response Filter ELM (FIR-ELM) training algorithm is proposed. The filtering technique is used to initialize the input weights of the Single Hidden Layer Feedforward Neural Network (SLFN). The hidden layer of SLFN is used as the preprocessor to achieve the minimum output error. To reduce the structural risk and empirical risk of SLFN, the simulation results of 300 groups of spectral data show that the improved FIR-ELM algorithm significantly improves the training accuracy and has good robustness compared with the traditional extreme learning machine algorithm.

Energy Management Systems (EMS) are indispensable for Smart Energy-Efficient Buildings (SEEB). This paper proposes a Wireless Sensor Network (WSN)-based EMS deployed and tested in a real-world smart building on a university campus. The at-scale implementation enabled the deployment of a WSN mesh topology to evaluate performance in terms of routing capabilities, data collection, and throughput. The proposed EMS uses the Context-Based Reasoning (CBR) Model to represent different types of buildings and offices. We implemented a new energy-efficient policy for electrical heaters control based on a Finite State Machine (FSM) leveraging on context-related events. This demonstrated significant effectiveness in minimizing the processing load, especially when adopting multithreading in data acquisition and control. To optimize sensors’ battery lifetime, we deployed a new Energy Aware Context Recognition Algorithm (EACRA) that dynamically configures sensors to send data under specific conditions and at particular times to avoid redundant data transmissions. EACRA increases the sensors’ battery lifetime by optimizing the number of samples, used modules, and transmissions. Our proposed EMS design can be used as a model to retrofit other kinds of buildings, such as residential and industrial, and thus converting them to SEEBs.

Diabetic retinopathy” is damage to retina denotes one of the problems of diabetes which is a significant reason for visual infirmity and blindness. A comprehensive and routine eye check is important to early detection and rapid treatment. This study proposes a hardware system that can enhance the contrast in the diabetic retinopathy eye fundus images as a first step in different eye disease diagnoses. The fuzzy histogram equalization technique is proposed to increases the local contrast of Diabetic Retinopathy Images. First, a histogram construction hardware architecture for different image processing purposes has been built then modified with fuzzy techniques to create fuzzy histogram equalization architecture, which is used to enhance the original images. Both architectures are designed using a finite-state machine (FSM) technique and programmed with VHDL programming language. The first one is implemented using two (Spartan 3E-XC3S500 and Xilinx Artix-7 XC7A100T) kits, while the second architecture is implemented using (Spartan 3E-XC3S500) kit. The system consists also of a modified video graphics array (VGA) port to display the input and resulted images with a proper resolution. All the hardware outputs are compared to that results produce from MatLab for verification and the resulted images are tested by PSNR, MSE, ENTROPY ,and AMBE

Quality of sleep can be assessed by analyzing the cyclic alternating pattern, a long-lasting periodic activity that is composed of two alternate electroencephalogram patterns, which is considered to be a marker of sleep instability. Experts usually score this pattern through a visual examination of each one-second epoch of an electroencephalogram signal, a repetitive and time-consuming task that is prone to errors. To address these issues, a home monitoring device was developed for automatic scoring of the cyclic alternating pattern by analyzing the signal from one electroencephalogram derivation. Three classifiers, specifically, two recurrent networks (long short-term memory and gated recurrent unit) and one one-dimension convolutional neural network, were developed and tested to determine which was more suitable for the cyclic alternating pattern phase’s classification. It was verified that the network based on the long short-term memory attained the best results with an average accuracy, sensitivity, specificity and area under the receiver operating characteristic curve of, respectively, 76%, 75%, 77% and 0.752. The classified epochs were then fed to a finite state machine to determine the cyclic alternating pattern cycles and the performance metrics were 76%, 71%, 84% and 0.778, respectively. The performance achieved is in the higher bound of the experts’ expected agreement range and considerably higher than the inter-scorer agreement of multiple experts, implying the usability of the device developed for clinical analysis.

A power management strategy is a key necessity for power-split electromechanical transmission systems. A model predictive control strategy which is based on finite-horizon optimization and can combine the advantages of instantaneous optimization and global optimization is a good solution for online optimization of the power management. Therefore, a model-predictive-control-based power management strategy is proposed for a two-mode electromechanical transmission. A model predictive control strategy consists of two parts: a predictive model and a receding-horizon optimization algorithm. A predictive model is used for predicting future information on the electromechanical transmission states, and real-time receding-horizon optimization with a finite horizon is adopted for optimal decision making. First, the predictive model, including the battery state and the transmission output torque, which provides a priori knowledge for optimal calculation, is proposed. Then, to ensure optimal operating areas of the engine and the motors, a novel overall efficiency calculation method for the whole powertrain including the engine, the motors, the power-split coupled machine and the battery is proposed and regarded as the optimization objective. The overall efficiency not only is focused on the engine fuel economy but also determines the power loss of the motors, the battery and the planetary gears together, which enhances the fuel economy and the transmission efficiency significantly. Based on the predictive model and receding-horizon optimization, the MPC strategy is established and tested by hardware-in-the-loop simulations under Urban Dynamometer Driving Schedule and New European Driving Cycle conditions. The test results showed that the power management strategy can enhance the fuel economy and proved to be a potential real-time optimization method for power distribution in the electromechanical transmission system; this strategy can provide theoretical support for actual application of electromechanical transmission systems.

In the Republic of Belarus, on the base of JSC “Soligorsk Institute for Resources Saving Problems with Pilot Production”, the development and launch of import-substituting mass production of large-capacity double-drum mine winders with an increased diameter of winding drums up to 7 meters were fulfilled. Earlier, in the post-Soviet space, machines with similar technical characteristics were not produced. Accordingly, for the implementation of the project, a comprehensive study of all the scientific and technical aspects of the creation of this equipment was required, taking into account the material and technical capabilities of the enterprises of domestic mining engineering. One of the important results of this study was the development of an improved methodology for calculating the basic elements of a winder. The technique allows calculating the strength parameters of the winding drums, shaft, disengagement mechanism, drive elements (mating clutch), bearings together with their anchor fasteners, reduced to normal operating conditions and to conditions of emergency wire breakage. The methodology includes the calculation of the stress-strain state of the elements of the winding drums using the finite element method and allows using the obtained numerical data to simulate the stress-strain state of the main components of the hoisting machine to calculate its safety factors in normal operation and in emergency conditions. The developed methodology was successfully tested in the design and technological center of JSC “Soligorsk Institute for Resources Saving Problems with Pilot Production” during the development of double-drum mine winders with large diameter winding drums for skip hoisting units of the Petrikov mining and processing complex, as well as during the modernization of existing hoisting units at working mines of JSC “Belaruskali”.

Robot cooperation is key in Search and Rescue (SaR) tasks. Frequently, these tasks take place in complex scenarios affected by different types of disasters, so an aerial viewpoint is useful for autonomous navigation or human tele-operation. In such cases, an Unmanned Aerial Vehicle (UAV) in cooperation with an Unmanned Ground Vehicle (UGV) can provide valuable insight into the area. To carry out its work successfully, such as multi-robot system requires the autonomous takeoff, tracking, and landing of the UAV on the moving UGV. Furthermore, it needs to be robust and capable of life-long operation. In this paper, we present an autonomous system that enables a UAV to take off autonomously from a moving landing platform, locate it using visual cues, follow it, and robustly land on it. The system relies on a finite state machine, which together with a novel re-localization module allows the system to operate robustly for extended periods of time and to recover from potential failed landing maneuvers. Two approaches for tracking and landing are developed, implemented, and tested. The first variant is based on a novel height-adaptive PID controller that uses the current position of the landing platform as the target. The second one combines this height-adaptive PID controller with a Kalman filter in order to predict the future positions of the platform and provide them as input to the PID controller. This facilitates tracking and, mainly, landing. Both the system as a whole and the re-localization module in particular have been tested extensively in a simulated environment (Gazebo). We also present a qualitative evaluation of the system on the real robotic platforms, demonstrating that our system can also be deployed on real robotic platforms. For the benefit of the community, we make our software open source.

Abstract In general, superconducting tokamaks require low loop voltage current start-up for the safety purpose of their poloidal field coils. The loop voltage inside the vacuum vessel of Steady State Superconducting Tokamak (SST-1) is low in nature since its central solenoid is located outside the cryostat. The low loop voltage current start-up of the SST-1 is routinely performed by using the electron cyclotron resonance (ECR) method at the toroidal magnetic field B t = 1.5 T (first harmonic) and 0.75 T (second harmonic). Recently, an alternative RF-based plasma current start-up system has been planned for operating the machine, especially for a higher toroidal magnetic field regime 1.5 T ⩽ B t ⩽ 3 T . The system was already developed based on an antenna system, made of a series of combinations of two flat spiral antennas, to assist plasma current start-up at a lower inductive electric field. It has already been tested and installed in the SST-1 chamber. The system testing was performed without a background magnetic field within the frequency regime of 35 MHz–60 MHz at present. The test results show that it can produce an electron density of n e ≃ 10 16 m − 3 measured by the Langmuir probe at the expense of 500 W RF power. The spectroscopy results indicate its capability of producing plasma density greater than 10 13 m − 3 and an electron temperature of T e = 2 –6 eV. In addition, the results also show the presence of a turbulent electric field of the order of 106 V m−1 at the antenna center and a finite anomalous temperature of neutral particles. Calculations show that the obtained density is sufficient for SST-1 low loop voltage plasma breakdown. The antenna system is also capable of producing plasma at higher frequencies. This article will discuss the development of the prototype and the installed antenna system along with their test results in detail.

Advancement in technology towards mobile computing and communication demands longer battery life, which mandates the low power design methodologies. In this paper, we have presented a novel low-power 8T flip-flop (FF) architecture, which has outsmarted the existing well-known dynamic, semi-dynamic and explicit pulsed flip-flops in terms of power and delay. The major ingredient of this architecture is a voltage keeper, which is incorporated to achieve reliable logic switching at the propagating nodes of the design. However, we have also come up with two new approaches of gated clock generation based on transmission gate (TG) and pass transistor logic (PTL) as a modification of LECTOR-based gating. These gating logics have proved themselves to be competent enough to reduce both the static and dynamic power dissipations and hence are employed to the proposed flip-flop to achieve further reduction in power than its nongated correspondent. The performance of this proposed gated flip-flop is tested in a finite state machine with its application in low-power serial adder design. All the simulations are carried out using 65-nm and 90-nm CMOS technologies with a power supply of 1.1[Formula: see text]V at 6.6[Formula: see text]GHz clock frequency. The gated FF saves 52.12%, 6.36% and 28.18% average power-using LECTOR, TG and PTLs, respectively, with respect to its nongated counterpart in 65-nm technology. The performance metrics of gated and nongated proposed designs are affirmed in the environment of commercialized CMOS foundry.

Multilevel inverters are a type of power electronic circuit that converts direct current (DC) to alternating current (AC) for use in high-voltage and high-power applications. Many recent studies on multilevel inverters have used field-programmable gate arrays (FPGAs) as a switching controller device to overcome the limitations of microcontrollers or DSPs, such as limited sampling rate, low execution speed, and a limited number of IO pins. However, the design techniques of most existing FPGA-based switching controllers require large amounts of memory (RAM) for storage of sampled data points as well as complex controller architectures to generate the output gating pulses. Therefore, in this paper, we propose two types of FPGA-based digital switching controllers, namely selective harmonic elimination (SHE) and sinusoidal pulse width modulation (SPWM), for a 21-level multilevel inverter. Both switching controllers were designed with minimal hardware complexity and logic utilisation. The designed SHE switching controller mainly consists of a four-bit finite state machine (FSM) and a 13-bit counter, while the SPWM switching controller employs a simple iterative CORDIC algorithm with a small amount of data storage requirement, a six-bit up-down counter, and a few adders. Initially, both digital switching controllers (SHE and SPWM) were designed using the hardware description language (HDL) in Verilog codes and functionally verified using the developed testbenches. The designed digital switching controllers were then synthesised and downloaded to the Intel FPGA (DE2-115) board for real-time verification purposes. For system-level verification, both switching controllers were tested on five cascaded H-Bridge circuits for a 21-level multilevel inverter model using the HDL co-simulation method in MATLAB Simulink. From the synthesised logic gates, it was found that the designed SHE and SPWM switching controllers require only 186 and 369 logic elements (LEs), respectively, which is less than 1% of the total LEs in an FPGA (Cyclone IV E) chip. The execution speed of the SHE switching controller implemented in the FPGA (Cyclone IV E) chip was found to be a maximum of 99.97% faster when compared with the microcontroller (PIC16F877A). The THD percentage of the 21-level SHE digital switching controller (3.91%) was found to be 37% less than that of the SPWM digital switching controller (6.17%). In conclusion, the proposed simplified design architectures of SHE and SPWM digital switching controllers have been proven to not only require minimal logic resources, achieve high processing speeds, and function correctly when tested on a real-time FPGA board, but also generate the desired 21-level stepped sine-wave output voltage (±360 VPP) at a frequency of 50 Hz with low THD percentages when tested on a 21-level cascaded H-Bridge multilevel inverter model.

An injury or disease of the central nervous system (CNS) results in significant limitations in the communication with the environment (e.g., mobility, reaching and grasping). Functional electrical stimulation (FES) externally activates the muscles; thus, can restore several motor functions and reduce other health related problems. This review discusses the major bottleneck in current FES which prevents the wider use and better outcome of the treatment. We present a control method that we continually enhance during more than 30 years in the research and development of assistive systems. The presented control has a multi-level structure where upper levels use finite state control and the lower level implements model based control. We also discuss possible communication channels between the user and the controller of the FES. The artificial controller can be seen as the replica of the biological control. The principle of replication is used to minimize the problems which come from the interplay of biological and artificial control in FES. The biological control relies on an extensive network of neurons sending the output signals to the muscles. The network is being trained though many the trial and error processes in the early childhood, but staying open to changes throughout the life to satisfy the particular needs. The network considers the nonlinear and time variable properties of the motor system and provides adaptation in time and space. The presented artificial control method implements the same strategy but relies on machine classification, heuristics, and simulation of model-based control. The motivation for writing this review comes from the fact that many control algorithms have been presented in the literature by the authors who do not have much experience in rehabilitation engineering and had never tested the operations with patients. Almost all of the FES devices available implement only open-loop, sensory triggered preprogrammed sequences of stimulation. The suggestion is that the improvements in the FES devices need better controllers which consider the overall status of the potential user, various effects that stimulation has on afferent and efferent systems, reflexive responses to the FES and direct responses to the FES by non-stimulated sensory-motor systems, and the greater integration of the biological control.

AbstractTopology optimization by optimally distributing materials in a given domain requires non-gradient optimizers to solve highly complicated problems. However, with hundreds of design variables or more involved, solving such problems would require millions of Finite Element Method (FEM) calculations whose computational cost is huge and impractical. Here we report Self-directed Online Learning Optimization (SOLO) which integrates Deep Neural Network (DNN) with FEM calculations. A DNN learns and substitutes the objective as a function of design variables. A small number of training data is generated dynamically based on the DNN’s prediction of the optimum. The DNN adapts to the new training data and gives better prediction in the region of interest until convergence. The optimum predicted by the DNN is proved to converge to the true global optimum through iterations. Our algorithm was tested by four types of problems including compliance minimization, fluid-structure optimization, heat transfer enhancement and truss optimization. It reduced the computational time by 2 ~ 5 orders of magnitude compared with directly using heuristic methods, and outperformed all state-of-the-art algorithms tested in our experiments. This approach enables solving large multi-dimensional optimization problems.

Reusing traction electric machine windings in electric vehicles as an integrated filtering inductor is a promising solution to reduce the size of the vehicle to grid (V2G) charging/discharging system. Obviously, the integrated inductors need to meet the requirements of traction and charging/discharging, which brings challenge for the design of traction machines. As one of the most popular traction electric machine types, the high-speed induction machine usually has large electrical time constant and consequently unacceptably long transient time in the design stage when finite element analysis is adopted. In this article, a method is proposed to quickly and accurately calculate the steady state performance of the induction machine by time-stepping transient magnetic finite element analysis. First, the stator current magnitude is ramped up from zero to full magnitude gradually to control the DC component in rotor flux and torque. Second, a multistep equivalent resistance method is adopted to decrease rotor time constant and suppress slot-tooth harmonic transient response. The proposed method can predict the FEA computation load before running the calculation, and it does not rely on the machine parameter and feedback signal. Its performance is tested by an example induction machine. The result shows that the proposed method can reduce the finite element calculation time of a high-speed operating point by 99%.

Traditional recruitment procedures are replaced in order to overcome most of its attendant challenges such as time-consuming and tiresome nature of recruiting a larger number of applicants into Civil Service from different parts of Nigeria considering multifaceted nature of the nation. This research work obliterate favouritism, nepotism and other corrupt means that were the usual practice in shortlisting prospective candidate for job, electronic recruitment system are enhanced with the availability of mobile platform that improve accessibility with emerging computing paradigm over the internet. Finite State Transducer (FST) in Machine Learning is used to learn from the pool of candidates for employment, GIATI(Grammar Inference and Alignment for Transducers Inference) are efficiently applied that is prospective candidates are offered job on a deductive rule-based Machine Learning. It is implemented with Android Studio and WAMP server. Its performance was tested using Ogun State as a pilot in the Federation and User Satisfaction were evaluated.

We describe a general and safe computational framework that provides integer programming results with the degree of certainty that is required for machine-assisted proofs of mathematical theorems. At its core, the framework relies on a rational branch-and-bound certificate produced by an exact integer programming solver, SCIP, in order to circumvent floating-point roundoff errors present in most state-of-the-art solvers for mixed-integer programs. The resulting certificates are self-contained and checker software exists that can verify their correctness independently of the integer programming solver used to produce the certificate. This acts as a safeguard against programming errors that may be present in complex solver software. The viability of this approach is tested by applying it to finite cases of Chvátal’s conjecture, a long-standing open question in extremal combinatorics. We take particular care to verify also the correctness of the input for this specific problem, using the Coq formal proof assistant. As a result we are able to provide a first machine-assisted proof that Chvátal’s conjecture holds for all downsets whose union of sets contains seven elements or less.

ObjectiveTo develop and implement a classifcation algorithm to identify likely acute opioid overdoses from text fields in emergency medical services (EMS) records.IntroductionOpioid overdoses have emerged within the last five to ten years to be a major public health concern. The high potential for fatal events, disease transmission, and addiction all contribute to negative outcomes. However, what is currently known about opioid use and overdose is generally gathered from emergency room data, public surveys, and mortality data. In addition, opioid overdoses are a non-reportable condition. As a result, state/national standardized procedures for surveillance or reporting have not been developed, and local government monitoring is frequently not specific enough to capture and track all opioid overdoses. Lastly, traditional means of data collection for conditions such as heart disease through hospital networks or insurance companies are not necessarily applicable to opioid overdoses, due to the often short disease course of addiction and lack of consistent health care visits. Overdose patients are also reluctant to follow-up or provide contact information due to law enforcement or personal reasons. Furthermore, collected data related to overdoses several months or years after the fact are useless in terms of short-term outreach. Therefore, given the potentially brief timeline of addiction or use to negative outcome, the current project set to create a near real-time surveillance and treatment/outreach system for opioid overdoses using an already existing EMS data collection framework.MethodsMarin County Department of Health and Human Services EMS data (2015-2017) was used for development of the system. The pool of data for model development and evaluation consisted of 15,000 EMS records randomly selected from 2015, 2016, and 2017. Each record was manually classified in a binary manner with the criteria of “more likely than not opioid related”, using only selected text fields. The event did not need to be exclusively opioid related, nor did opioids have to be the primary cause for the EMS call. 2,000 records were selected for review by the medical director for Marin County EMS, with a Cohen’s kappa coefficient of approximately 0.94. Overall, the proportion of opioid overdoses was less than 0.01 amongst the 15,000 records. An enriched data set of 80 randomly selected overdoses and 320 randomly selected non-overdoses was created for the purposes of feature engineering. These 400 records were excluded for further use in model training and testing. Within the enriched set, the descriptive text fields were tokenized based on the hypothesis that opioid overdoses and non-overdoses are separable based on the content of the descriptive fields. Each field was tokenized as words, bigrams (pairs of consecutive words), and trigrams (triplets of consecutive words). The frequencies of each token as a percentage of overall words were calculated separately for opioid overdoses and non-overdoses. Structured fields used in the analysis were not tokenized prior to frequency calculations. The frequencies for each token/phrase were then compared across opioid overdoses status with a proportion test for equality at an alpha of 0.05 with a Bonferroni correction for multiple comparisons. The tokens/phrases that were statistically significantly more likely to be present in opioid overdoses were assigned to a quintile based on their p-value, with smallest p-values assigned five, and largest p-values assigned one. Tokens/phrases statistically significantly more likely to be present in non-overdoses were scored in the same manner, with the smallest p-value assigned negative five, and the largest p-value negative one. The tokens/phrases that were statistically different across opioid overdose status were stored along with their quintile scores in dictionaries that were kept for future modeling use. From the initial 15,000 classified records, excluding the 400 used for the enriched data set, 10,000 records were randomly selected for model training and development. Each record had their text fields tokenized into words, bigrams, and trigrams, and each was compared with the corresponding dictionary. If a token was present in the entry and also in the dictionary, that token’s quintile score was assigned to the record, with multiple tokens being summed to produce a score for each field-token option. The final created feature was the count of opioid specific terms such as “heroin”, “fentanyl”, “narcan”, etc. within the main narrative field. The intent was to create a variety of numerical features that were indicative of presence of tokens/phrases that were positively associated with opioid overdoses such that higher scores were more associated. Several models including support vector machines, neural nets, gradient boosted machines, and logistic regression were tested via 10-fold cross validation, with logistic regression yielding the best error rates and lowest computational costs. Although all models resulted in a sensitivity greater than 85 percent, logistic regression was by far the best in terms of false positive rate. The coefficients for the logistic regression model were selected from the eight created features along with patient sex and patient age by best subsets selection via Akaike information criterion (AIC), and the probability threshold for classification was selected via optimizing the receiver operating curve (ROC).ResultsFollowing the variable selection and threshold optimization for logistic regression, the sensitivity and specificity of the model were between 90 percent and 95 percent. However, given the large number of records fed through the algorithm either each week for 'real-time' surveillance and treatment/outreach, or for larger retrospective data sets, improving specificity is crucial to reduce the number of false positives. Additionally, given that a public health treatment/outreach staff has a finite amount of time and resources, limiting false positives will allow them to focus on the true cases. Further model improvements were made with a series of binary filters that allowed for overall sensitivity/specificity improvements as well as ensuring that the records sent for outreach are appropriate for outreach. The application of the filters pushed the classification sensitivity and specificity to greater than 99 percent. Further, the filters removed cases inappropriate for outreach at greater than 90 percent efficiency.ConclusionsThe algorithm was able to classify opioid overdoses in EMS data with a sensitivity and specificity greater than 99 percent. It was implemented into a viable public health treatment/outreach system through the Marin County Department of Health and Human Services in May 2018, and has identified approximately 50 overdoses for outreach as of September, 2018. It is possible, using minimal computational power and infrastructure to develop a fully realized surveillance system through EMS data for nearly any size public health entity. Additionally, the framework allows for flexibility such that the system can be tailored for specific clinical or surveillance needs - there is no 'black box' component. Lastly, the application of this methodology to other diseases/conditions is possible and has already been done using the same data for both sepsis and falls in older adults.References1) R Core Team. 2018. R: A Language and Environment for Statistical Computing. Available: https://www.r-project.org/.2) RStudio Team. 2018. RStudio: Integrated Development Environment for R. Available: http://www.rstudio.com/.

The contemporary video game market is as recognisable for its brands as it is for the characters that populate their game worlds, from franchise-leading characters like Garrus Vakarian (Mass Effect original trilogy), Princess Zelda (The Legend of Zelda franchise) and Cortana (HALO franchise) to more recent game icons like Miles Morales (Marvel's Spiderman game franchise) and Judy Alvarez (Cyberpunk 2077). Interactions with these casts of characters enhance the richness of games and their playable worlds, giving a sense of weight and meaning to player actions, emphasising thematic interests, and in some cases acting as buffers to (or indeed hindering) different aspects of gameplay itself. As Jordan Erica Webber writes in her essay The Road to Journey, “videogames are often examined through the lens of what you do and what you feel” (14). For many games, the design of interactions between the player and other beings in the world—whether they be intrinsic to the world (non-playable characters or NPCs) or other live players—is a bridging aspect between what you do and how you feel and is thus central to the communication of more cohesive and focussed work. This essay will discuss two examples of game design techniques present in Transistor by Supergiant Games and Journey by thatgamecompany. It will consider how the design of “playful” interactions between the player and other characters in the game world (both non-player characters and other player characters) can be used as a tool to align a player’s experience of “intent” with the thematic objectives of the designer. These games have been selected as both utilise design techniques that allow for this “playful” interaction (observed in this essay as interactions that do not contribute to “progression” in the traditional sense). By looking closely at specific aspects of game design, it aims to develop an accessible examination by “focusing on the dimensions of involvement the specific game or genre of games affords” (Calleja, 222). The discussion defines “intent”, in the context of game design, through a synthesis of definitions from two works by game designers. The first being Greg Costikyan’s definition of game structure from his 2002 presentation I Have No Words and I Must Design, a paper subsequently referenced by numerous prominent game scholars including Ian Bogost and Jesper Juul. The second is Steven Swink’s definition of intent in relation to video games, from his 2009 book Game Feel: A Game Designer’s Guide to Virtual Sensation—an extensive reference text of game design concepts, with a particular focus on the concept of “game feel” (the meta-sensation of involvement with a game). This exploratory essay suggests that examining these small but impactful design techniques, through the lens of their contribution to overall intent, is a useful tool for undertaking more holistic studies of how games are affective. I align with the argument that understanding “playfulness” in game design is useful in understanding user engagement with other digital communication platforms. In particular, platforms where the presentation of user identity is relational or performative to others—a case explored in Playful Identities: The Ludification of Digital Media Cultures (Frissen et al.). Intent in Game Design Intent, in game design, is generated by a complex, interacting economy, ecosystem, or “game structure” (Costikyan 21) of thematic ideas and gameplay functions that do not dictate outcomes, but rather guide behaviour and progression forward through the need to achieve a goal (Costikyan 21). Intent brings player goals in line with the intrinsic goals of the player character, and the thematic or experiential goals the game designer wants to convey through the act of play. Intent makes it easier to invest in the game’s narrative and spatial context—its role is to “motivate action in game worlds” (Swink 67). Steven Swink writes that it is the role of game design to create compelling intent from “a seemingly arbitrary collection of abstracted variables” (Swink 67). He continues that whether it is good or bad is a broader question, but that “most games do have in-born intentionality, and it is the game designer who creates it” (67). This echoes Costikyan’s point: game designers “must consciously set out to decide what kind of experiences [they] want to impart to players and create systems that enable those experiences” (20). Swink uses Mario 64 as one simple example of intent creation through design—if collecting 100 coins did not restore Mario’s health, players would simply not collect them. Not having health restricts the ability for players to fulfil the overarching intent of progression by defeating the game’s main villain (what he calls the “explicit” intent), and collecting coins also provides a degree of interactivity that makes the exploration itself feel more fulfilling (the “implicit” intent). This motivation for action may be functional, or it may be more experiential—how a designer shapes variables into particular forms to encourage the particular kinds of experience that they want a player to have during the act of play (such as in Journey, explored in the latter part of this essay). This essay is interested in the design of this compelling thematic intent—and the role “playful” interactions have as a variable that contributes to aligning player behaviours and experience to the thematic or experiential goals of game design. “Playful” Communication and Storytelling in Transistor Transistor is the second release from independent studio Supergiant Games and has received over 100 industry accolades (Kasavin) since its publication in 2014. Transistor incorporates the suspense of turn-based gameplay into an action role-playing game—neatly mirroring a style of gameplay to the suspense of its cyber noir narrative. The game is also distinctly “artful”. The city of Cloudbank, where the game takes place, is a cyberpunk landscape richly inspired by art nouveau and art deco style. There is some indication that Cloudbank may not be a real city at all—but rather a virtual city, with an abundance of computer-related motifs and player combat abilities named as if they were programming functions. At release, Transistor was broadly recognised in the industry press for its strength in “combining its visuals and music to powerfully convey narrative information and tone” (Petit). If intent in games in part stems from a unification of goals between the player and design, the interactivity between player input and the actions of the player character furthers this sense of “togetherness”. This articulation and unity of hand movement and visual response in games are what Kirkpatrick identified in his 2011 work Aesthetic Theory and the Video Game as the point in which videogames “broke from the visual entertainment culture of the last two centuries” (Kirkpatrick 88). The player character mediates access to the space by which all other game information is given context and allows the player a degree of self-expression that is unique to games. Swink describes it as an amplified impression of virtual proprioception, that is “an impression of space created by illusory means but is experienced as real by the senses … the effects of motion, sound, visuals, and responsive effects combine” (Swink 28). If we extend Swink’s point about creating an “impression of space” to also include an “impression of purpose”, we can utilise this observation to further understand how the design of the playful interactions in Transistor work to develop and align the player’s experience of intent with the overarching narrative goal (or, “explicit” intent) of the game—to tell a compelling “science-fiction love story in a cyberpunk setting, without the gritty backdrop” (Wallace) through the medium of gameplay. At the centre of any “love story” is the dynamic of a relationship, and in Transistor playful interaction is a means for conveying the significance and complexity of those dynamics in relation to the central characters. Transistor’s exposition asks players to figure out what happened to Red and her partner, The Boxer (a name he is identified by in the game files), while progressing through various battles with an entity called The Process to uncover more information. Transistor commences with player-character, Red, standing next to the body of The Boxer, whose consciousness and voice have been uploaded into the same device that impaled him: the story’s eponymous Transistor. The event that resulted in this strange circumstance has also caused Red to lose her ability to speak, though she is still able to hum. The first action that the player must complete to progress the game is to pull the Transistor from The Boxer’s body. From this point The Boxer, speaking through the Transistor, becomes the sole narrator of the game. The Boxer’s first lines of dialogue are responsive to player action, and position Red’s character in the world: ‘Together again. Heh, sort of …’ [Upon walking towards an exit a unit of The Process will appear] ‘Yikes … found us already. They want you back I bet. Well so do I.’ [Upon defeating The Process] ‘Unmarked alley, east of the bay. I think I know where we are.’ (Supergiant Games) This brief exchange and feedback to player movement, in medias res, limits the player’s possible points of attention and establishes The Boxer’s voice and “character” as the reference point for interacting with the game world. Actions, the surrounding world, and gameplay objectives are given meaning and context by being part of a system of intent derived from the significance of his character to the player character (Red) as both a companion and information-giver. The player may not necessarily feel what an individual in Red’s position would feel, but their expository position is aligned with Red’s narrative, and their scope of interaction with the world is intrinsically tied to the “explicit” intent of finding out what happened to The Boxer. Transistor continues to establish a loop between Red’s exploration of the world and the dialogue and narration of The Boxer. In the context of gameplay, player movement functions as the other half of a conversation and brings the player’s control of Red closer to how Red herself (who cannot communicate vocally) might converse with The Boxer gesturally. The Boxer’s conversational narration is scripted to occur as Red moves through specific parts of the world and achieves certain objectives. Significantly, The Boxer will also speak to Red in response to specific behaviours that only occur should the player choose to do them and that don’t necessarily contribute to “progressing” the game in the mechanical sense. There are multiple points where this is possible, but I will draw on two examples to demonstrate. Firstly, The Boxer will have specific reactions to a player who stands idle for too long, or who performs a repetitive action. Jumping repeatedly from platform to platform will trigger several variations of playful and exasperated dialogue from The Boxer (who has, at this point, no choice but to be carried around by Red): [Upon repeatedly jumping between the same platform] ‘Round and round.’ ‘Okay that’s enough.’ ‘I hate you.’ (Supergiant Games) The second is when Red “hums” (an activity initiated by the player by holding down R1 on a PlayStation console). At certain points of play, when making Red hum, The Boxer will chime in and sing the lyrics to the song she is humming. This musical harmonisation helps to articulate a particular kind of intimacy and flow between Red and The Boxer —accentuated by Red’s animation when humming: she is bathed in golden light and holds the Transistor close, swaying side to side, as if embracing or dancing with a lover. This is a playful, exploratory interaction. It technically doesn’t serve any “purpose” in terms of finishing the game—but is an action a player might perform while exploring controls and possibilities of interactivity, in turn exploring what it is to “be” Red in relation to the game world, the story being conveyed, and The Boxer. It delivers a more emotional and affective thematic idea about a relationship that nonetheless relies just as much on mechanical input and output as engaging in movement, exploration, and combat in the game world. It’s a mechanic that provides texture to the experience of inhabiting Red’s identity during play, showcasing a more individual complexity to her story, driven by interactivity. In techniques like this, Transistor directly unifies its method for information-giving, interactivity, progression, and theme into a single design language. To once again nod to Swink and Costikyan, it is a complex, interacting economy or ecosystem of thematic ideas and gameplay structures that guide behaviour and progression forward through the need to achieve a single goal (Costikyan 21), guiding the player towards the game’s “explicit” intent of investment in its “science fiction love story”. Companionship and Collaboration in Journey Journey is regularly praised in many circles of game review and discussion for its powerful, pared-back story conveyed through its exceptional game design. It has won a wide array of awards, including multiple British Academy Games Awards and Game Developer’s Choice Awards, and has been featured in highly regarded international galleries such as the Victoria and Albert Museum in London. Its director, Jenova Chen, articulated that the goal of the game (and thus, in the context of this essay, the intent) was “to create a game where people who interact with each other in an online community can connect at an emotional level, regardless of their gender, age, ethnicity, and social status” (Webber 14). In Journey, the player controls a small robed figure moving through a vast desert—the only choices for movement are to slide gracefully through the sand or to jump into the air by pressing the X button (on a PlayStation console), and gracefully float down to the ground. You cannot attack anything or defend yourself from the elements or hostile beings. Each player will “periodically find another individual in the landscape” (Isbister 121) of similar design to the player and can only communicate with them by experimenting with simple movements, and via short chirping noises. As the landscape itself is vast and unknown, it is what one player referred to as a sense of “reliance on one another” that makes the game so captivating (Isbister 12). Much like The Boxer in Transistor, the other figure in Journey stands out as a reference point and imbues a sense of collaboration and connection that makes the goal to reach the pinprick of light in the distance more meaningful. It is only after the player has finished the game that the screen reveals the other individual is a real person, another player, by displaying their gamer tag. One player, playing the game in 2017 (several years after its original release in 2012), wrote: I went through most of the game by myself, and when I first met my companion, it was right as I walked into the gate transitioning to the snow area. And I was SO happy that there was someone else in this desolate place. I felt like it added so much warmth to the game, so much added value. The companion and I stuck together 100% of the way. When one of us would fall the slightest bit behind, the other would wait for them. I remember saying out loud how I thought that my companion was the best programmed AI that I had ever seen. In the way that he waited for me to catch up, it almost seemed like he thanked me for waiting for him … We were always side-by-side which I was doing to the "AI" for "cinematic-effect". From when I first met him up to the very very end, we were side-by-side. (Peace_maybenot) Other players indicate a similar bond even when their companion is perhaps less competent: I thought my traveller was a crap AI. He kept getting launched by the flying things and was crap at staying behind cover … But I stuck with him because I was like, this is my buddy in the game. Same thing, we were communicating the whole time and I stuck with him. I finish and I see a gamer tag and my mind was blown. That was awesome. (kerode4791) Although there is a definite object of difference in that Transistor is narrated and single-player while Journey is not, there are some defined correlations between the way Supergiant Games and thatgamecompany encourage players to feel a sense of investment and intent aligned with another individual within the game to further thematic intent. Interactive mechanics are designed to allow players a means of playful and gestural communication as an extension of their kinetic interaction with the game; travellers in Journey can chirp and call out to other players—not always for an intrinsic goal but often to express joy, or just to experience and sense of connectivity or emotional warmth. In Transistor, the ability to hum and hear The Boxer’s harmony, and the animation of Red holding the Transistor close as she does so, implying a sense of protectiveness and affection, says more in the context of “play” than a literal declaration of love between the two characters. Graeme Kirkpatrick uses dance as a suitable metaphor for this kind of experience in games, in that both are characterised by a certainty that communication has occurred despite the “eschewal of overt linguistic elements and discursive meanings” (120). There is also a sense of finite temporality in these moments. Unlike scripted actions, or words on a page, they occur within a moment of being that largely belongs to the player and their actions alone. Kirkpatrick describes it as “an inherent ephemerality about this vanishing and that this very transience is somehow essential” (120). This imbuing of a sense of time is important because it implies that even if one were to play the game again, repeating the interaction is impossible. The communication of narrative within these games is not a static form, but an experience that hangs unique at that moment and space of play. Thatgamecompany discussed in their 2017 interviews with Webber, published as part of her essay for the Victoria & Albert’s Video Games: Design/Play/Disrupt exhibition, how by creating and restricting the kind of playful interaction available to players within the world, they could encourage the kind of emotional, collaborative, and thoughtful intent they desired to portray (Webber 14). They articulate how in the development process they prioritised giving the player a variety of responses for even the smallest of actions and how that positive feedback, in turn, encourages play and prevented players from being “bored” (Webber 22). Meanwhile, the team reduced responsiveness for interactions they didn’t want to encourage. Chen describes the approach as “maximising feedback for things you want and minimising it for things you don’t want” (Webber 27). In her essay, Webber writes that Chen describes “a person who enters a virtual world, leaving behind the value system they’ve learned from real life, as like a baby banging their spoon to get attention” (27): initially players could push each other, and when one baby [player] pushed the other baby [player] off the cliff that person died. So, when we tested the gameplay, even our own developers preferred killing each other because of the amount of feedback they would get, whether it’s visual feedback, audio feedback, or social feedback from the players in the room. For quite a while I was disappointed at our own developers’ ethics, but I was able to talk to a child psychologist and she was able to clarify why these people are doing what they are doing. She said, ‘If you want to train a baby not to knock the spoon, you should minimise the feedback. Either just leave them alone, and after a while they’re bored and stop knocking, or give them a spoon that does not make a sound. (27) The developers then made it impossible for players to kill, steal resources from, or even speak to each other. Players were encouraged to stay close to each other using high-feedback action and responsiveness for doing so (Webber 27). By using feedback design techniques to encourage players to behave a certain way to other beings in the world—both by providing and restricting playful interactivity—thatgamecompany encourage a resonance between players and the overarching design intent of the project. Chen’s observations about the behaviour of his team while playing different iterations of the game also support the argument (acknowledged in different perspectives by various scholarship, including Costikyan and Bogost) that in the act of gameplay, real-life personal ethics are to a degree re-prioritised by the interactivity and context of that interactivity in the game world. Intent and the “Actualities of (Game) Existence” Continuing and evolving explorations of “intent” (and other parallel terms) in games through interaction design is of interest for scholars of game studies; it also is an important endeavour when considering influential relationships between games and other digital mediums where user identity is performative or relational to others. This influence was examined from several perspectives in the aforementioned collection Playful Identities: The Ludification of Digital Media Cultures, which also examined “the process of ludification that seems to penetrate every cultural domain” of modern life, including leisure time, work, education, politics, and even warfare (Frissen et al. 9). Such studies affirm the “complex relationship between play, media, and identity in contemporary culture” and are motivated “not only by the dominant role that digital media plays in our present culture but also by the intuition that ‘“play is central … to media experience” (Frissen et al. 10). Undertaking close examinations of specific “playful” design techniques in video games, and how they may factor into the development of intent, can help to develop nuanced lines of questioning about how we engage with “playfulness” in other digital communication platforms in an accessible, comparative way. We continue to exist in a world where “ludification is penetrating the cultural domain”. In the first few months of the global COVID-19 pandemic, Nintendo released Animal Crossing: New Horizons. With an almost global population in lockdown, Animal Crossing became host to professional meetings (Espiritu), weddings (Garst), and significantly, a media channel for brands to promote content and products (Deighton). TikTok, panoramically, is a platform where “playful” user trends— dances, responding to videos, the “Tell Me … Without Telling Me” challenge—occur in the context of an extremely complex algorithm, that while automated, is created by people—and is thus unavoidably embedded with bias (Dias et al.; Noble). This is not to say that game design techniques and broader “playful” design techniques in other digital communication platforms are interchangeable by any measure, or that intent in a game design sense and intent or bias in a commercial sense should be examined through the same lens. Rather that there is a useful, interdisciplinary resource of knowledge that can further illuminate questions we might ask about this state of “ludification” in both the academic and public spheres. We might ask, for example, what would the implications be of introducing an intent design methodology similar to Journey, but using it for commercial gain? Or social activism? Has it already happened? There is a quotation from Nathan Jurgensen’s 2016 essay Fear of Screens (published in The New Inquiry) that often comes to my mind when thinking about interaction design in video games in this way. In his response to Sherry Turkle’s book, Reclaiming Conversation, Jurgensen writes: each time we say “IRL,” “face-to-face,” or “in person” to mean connection without screens, we frame what is “real” or who is a person in terms of their geographic proximity rather than other aspects of closeness — variables like attention, empathy, affect, erotics, all of which can be experienced at a distance. We should not conceptually preclude or discount all the ways intimacy, passion, love, joy, pleasure, closeness, pain, suffering, evil and all the visceral actualities of existence pass through the screen. “Face to face” should mean more than breathing the same air. (Jurgensen) While Jurgensen is not talking about communication in games specifically, there are comparisons to be drawn between his “variables” and “visceral actualities of existence” as the drivers of social meaning-making, and the methodology of games communicating intent and purpose through Swink’s “seemingly arbitrary collection of abstracted variables” (67). When players interact with other characters in a game world (whether they be NPCs or other players), they are inhabiting a shared virtual space, and how designers articulate and present the variables of “closeness”, as Jurgensen defines it, can shape player alignment with the overarching design intent. These design techniques take the place of Jurgensen’s “visceral actualities of existence”. While they may not intrinsically share an overarching purpose, their experiential qualities have the ability to align ethics, priorities, and values between individuals. Interactivity means game design has the potential to facilitate a particular kind of engagement for the player (as demonstrated in Journey) or give opportunities for players to explore a sense of what an emotion might feel like by aligning it with progression or playful activity (as discussed in relation to Transistor). Players may not “feel” exactly what their player-characters do, or care for other characters in the world in the same way a game might encourage them to, but through thoughtful intent design, something of recognition or unity of belief might pass through the screen. References Bogost, Ian. Persuasive Games: The Expressive Power of Video Games. MIT P, 2007. Calleja, Gordon. “Ludic Identities and the Magic Circle.” Playful Identities: The Ludification of Digital Media Cultures. Eds. Valerie Frissen et al. Amsterdam UP, 2015. 211–224. Costikyan, Greg. “I Have No Words & I Must Design: Toward a Critical Vocabulary for Games.” Computer Games and Digital Cultures Conference Proceedings 2002. Ed. Frans Mäyrä. Tampere UP. 9-33. Dias, Avani, et al. “The TikTok Spiral.” ABC News, 26 July 2021. <https://www.abc.net.au/news/2021-07-26/tiktok-algorithm-dangerous-eating-disorder-content-censorship/100277134>. Deighton, Katie. “Animal Crossing Is Emerging as a Media Channel for Brands in Lockdown.” The Drum, 21 Apr. 2020. <https://www.thedrum.com/news/2020/04/21/animal-crossing-emerging-media-channel-brands-lockdown>. Espiritu, Abby. “Japanese Company Attempts to Work Remotely in Animal Crossing: New Horizons.” The Gamer, 29 Mar. 2020. <https://www.thegamer.com/animal-crossing-new-horizons-work-remotely/>. Frissen, Valerie, et al., eds. Playful Identities: The Ludification of Digital Media Cultures. Amsterdam UP, 2015. Garst, Aron. “The Pandemic Canceled Their Wedding. So They Held It in Animal Crossing.” The Washington Post, 2 Apr. 2020. <https://www.washingtonpost.com/video-games/2020/04/02/animal-crossing-wedding-coronavirus/>. Isbister, Katherine. How Games Move Us: Emotion by Design. MIT P, 2016. Journey. thatgamecompany. 2012. Jurgensen, Nathan. “Fear of Screens.” The New Inquiry, 25 Jan. 2016. <https://thenewinquiry.com/fear-of-screens/>. Kasavin, Greg. “Transistor Earns More than 100+ Industry Accolades, Sells More than 600k Copies.” Supergiant Games, 8 Jan. 2015. <https://www.supergiantgames.com/blog/transistor-earns60-industry-accolades-sells-more-than-600k-copies/>. kerode4791. "Wanted to Share My First Experience with the Game, It Was That Awesome.”Reddit, 22 Mar. 2017. <https://www.reddit.com/r/JourneyPS3/comments/60u0am/wanted_to_share_my_f rst_experience_with_the_game/>. Kirkpatrick, Graeme. Aesthetic Theory and the Video Game. Manchester UP, 2011. Noble, Safiya Umoja. Algorithms of Oppression: How Search Engines Reinforce Racism. New York UP, 2018. peace_maybenot. "Wanted to Share My First Experience with the Game, It Was that Awesome” Reddit, 22 Mar. 2017. <https://www.reddit.com/r/JourneyPS3/comments/60u0am/wanted_to_share_my_f rst_experience_with_the_game/>. Petit, Carolyn. “Ghosts in the Machine." Gamespot, 20 May 2014. <https://www.gamespot.com/reviews/transistor-review/1900-6415763/>. Swink, Steve. Game Feel: A Game Designer’s Guide to Virtual Sensation. Amsterdam: Morgan Kaufmann Publishers/Elsevier, 2009. Transistor. Supergiant Games. 2014. Wallace, Kimberley. “The Story behind Supergiant Games’ Transistor.” Gameinformer, 20 May 2021. <https://www.gameinformer.com/2021/05/20/the-story-behind-supergiant-games-transistor>. Webber, Jordan Erica. “The Road to Journey.” Videogames: Design/Play/Disrupt. Eds. Marie Foulston and Kristian Volsing. V&A Publishing, 2018. 14–31.