Input Units Research Articles

A network model of behavioural performance in a rule learning task.

Humans demonstrate differences in performance on cognitive rule learning tasks which could involve differences in properties of neural circuits. An example model is presented to show how gating of the spread of neural activity could underlie rule learning and the generalization of rules to previously unseen stimuli. This model uses the activity of gating units to regulate the pattern of connectivity between neurons responding to sensory input and subsequent gating units or output units. This model allows analysis of network parameters that could contribute to differences in cognitive rule learning. These network parameters include differences in the parameters of synaptic modification and presynaptic inhibition of synaptic transmission that could be regulated by neuromodulatory influences on neural circuits. Neuromodulatory receptors play an important role in cognitive function, as demonstrated by the fact that drugs that block cholinergic muscarinic receptors can cause cognitive impairments. In discussions of the links between neuromodulatory systems and biologically based traits, the issue of mechanisms through which these linkages are realized is often missing. This model demonstrates potential roles of neural circuit parameters regulated by acetylcholine in learning context-dependent rules, and demonstrates the potential contribution of variation in neural circuit properties and neuromodulatory function to individual differences in cognitive function.This article is part of the theme issue 'Diverse perspectives on diversity: multi-disciplinary approaches to taxonomies of individual differences'.

ANALISIS KINERJA INDUSTRI MANUFAKTUR DI JAWA TENGAH: APLIKASI DEA MODEL VRS

This study aims to estimate the level of efficiency of the manufacturing industry in Central Java Province. The position of the manufacturing industry in Central Java between two clusters of Jakarta-Bandung and Surabaya were the justification for the urgency of this research. The concept of relative efficiency with format Constant Return to Scale (CRS) and the Variable Return to Scale (VRS) was used as the theoretical framework of this research. The concept was applied by the method of Data Envelopment analysis (DEA) to describe the level of efficiency of the manufacturing industry sub-sector in Central Java province in 2016. The study also describes a way of increasing efficiency in production output for the manufacturing sub-sector classified as inefficient. DEA model used in this study is the CRS and VRS with the orientation of the output (output-oriented)The results of this study indicate that the apparel sub-sector; wood and processing; machinery and equipment; as well as furniture and processing is a sub-sector of manufacturing industry in Central Java in 2016 were classified as inefficient. Sub sectors using too much input unit, thus creating inefficient production levels relative to other sub-sectors.

Component crop physiology and water use efficiency in response to intercropping

Abstract Interspecies specific interactions are generally regarded as drivers of plant productivity in multispecies agroecosystems. Complementary use of resource in diverse communities can enhance community productivity through optimal use of plant-available resources and positive interactions such as facilitation can ameliorate high abiotic stress conditions. We studied the effects on physiological response, leaf traits and water use efficiency of a multifunctional species intercropping system consisting of peanut (Arachis hypogaea L.), watermelon [Citrullus lanatus (Thunb.) Matsum. & Nakai], okra [Abelmoschus esculentus (L.) Moench], cowpea [Vigna unguiculata (L.) Walp.], and pepper (Capsicum annuum L.) planted alone or in various intercropping combinations in a low fertilizer input system in the peak of summer heat in Texas. Differences in gas exchange measurements were detected only in watermelon in year 2 of the study when okra was the dominant crop. This same year watermelon specific leaf area (SLA) was significantly higher when okra was present in a treatment and particularly in the three and four species combinations, Wpwo and Wpwoc, 27.5 and 31.0 m2 kg−1, respectively, as compared to watermelon grown in monoculture, strip intercropped with peanut (Spw) and within row intercropped with peanut (Wpw), 20.4, 20.1, and 19.8 m2 kg−1, respectively. This corresponds with an increase watermelon leaf N concentration and a decrease in leaf C:N ratio in Wpwo and Wpwoc treatments. No differences in d13C composition, a measure of water use efficiency over the leaf lifespan, were detected across cropping system for each species. Water use efficiency based on per plant production (WUEyield) indicated an increase in water use efficiency in dominant crops such as watermelon in 2011 and okra in 2012, but a reduction in WUEyield subordinate crops such as cowpea and pepper both years of the study. Peanut grown in monoculture and strip intercropped with watermelon had significantly lower leaf water potential values in 2012, −2.2 and −2.1 MPa, respectively, as compared to intercropping systems increasing in level of integration (Wpw = − 1.1, Wpwo = − 0.6, Wpwoc = − 1.3, Wall = −1.1 MPa), indicating peanut benefited from alterations to microclimate and facilitative interactions with companion crops in some intercropping systems through a reduction in plant water stress. The results from this study suggest there may be a benefit to a multifunctional intercropping system in the form of increased food production per unit of water input in dominant crops and reduced water stress for some component species. This is important to producers; showing a method to increase overall crop production without increasing water inputs.

Optimization of Hydraulic Servo Parameter for Semi-Solid Machine using Aerotech A3200

<p><em>Abstract</em><strong> </strong>– <strong>Hydraulics machines are important parts in semi solid metal forging machine, to form the workpiece by pressing. Semi-solid machine positioning movements are required to have a high accuration, to get the high accuracy of the movement system a servo hydraulic control is added ,which consist of servo valve and digital controller (Aerotech A3200). The digital control parameters (kpos, kp, ki) are used to enhanced the hydraulic movement. To get the effect of the control parameters of the plant (hydraulic servo), a mathematical approach is used for simulation in matlab. In this research, study was done by optimize kp and ki parameters of the control system. Prior to the optimization, simulation was conducted using matlab. Where the simulated testing is using the input unit (step) and frequency (bode) to predict the circumstances that occurred to the plan which is then compared with experimental results. a step response was simulated in matlab to analyze the error of the hydraulic system. Having obtained the minimum error from adjusting kp and ki in matlab, a trend graphic error was used as a guideline to adjust parameter in actual system. The last step is to implement and adjust kp/ki in actual system and measure the real position error.Based on this research, it concluded that the error before adjustment are 0.6118-1.2642 mm for autotune parameter kp 3500000, ki 50000. The result of average error in this experiments after adjustment are 0.017-0.166 mm for parameter kp 250000, ki 10000.</strong></p><p> </p><p><strong><em>Keywords</em></strong><em> - Hydraulic Servo, Aerotech A3200, Step response, Bode plot, Semi-Solid Machine</em>.</p>

PROTOCOL: Interventions to promote technology adoption in firms: a systematic review.

PROTOCOL: Interventions to promote technology adoption in firms: a systematic review.

ACTIVE POWER MEASUREMENT ACCURACY INCREASING WITH SIGNIFICANT REACTIVE COMPONENT

The broadband wattmeter of transient power with its own consumption error correction for measuring the active power against the background of large reactive components in the frequency range up to units MHz is described in the article. This instrument can measure the power loss (active power) in a variety of reactive elements, for example, the inductances used in pulsed energy converters. Such wattmeter can be used to optimize the development of wireless power transmission systems based on open resonance circuits. The structure of such systems, as was found during the study, has a spherical directional pattern and does not depend on the emitter and receiver location, described in the article. The article specifies the features of active power measurements at searching for the most efficient design of wireless power transmission systems on the basis of open resonant circuits, where the measurement is complicated by the fact that relatively small active power needs to be measured against the background of large reactive components, when the operating frequencies are shifted from the resonance, and phase shift between current and voltage accrue.At high frequencies measuring, the channels of the wattmeter become very sensitive to phase errors. It is necessary to minimize links that cause phase shifts. In this connection, it is important to construct correctly the wattmeter input units and to make such a transformation, which will provide operations with signal modules. The proposed scheme allows this to be achieved, and required gain is carried out without taking into account the signals phase shift.In the basic scheme of the wattmeter, the input device is executed from the main shunt and parallel to the auxiliary distributed shunt connected to it, which is connected to the main resistance of the voltage divider, in which the value of all resistances of the input device is determined by conditions of invariance to the squares of the voltage and load current, and the partially distributed auxiliary shunt is calculated with additional weighting factors to provide the conditions for invariance of the square of voltage and current and eliminate the error from its own revives.The value of the calculated power measurement error on the model for this wattmeter is 0,1%. The basic error of the wattmeter is the error of the low-ohm shunt. It is expedient to use coaxial or triaxial shunts to extend the frequency range. The designs of such shunts and examples of shunts with the best technical characteristics for today are given in the article.The advantages of such wattmeter are:- gives the exact value of the measured power on the load, regardless of the actual consumption of the input device;- the values of the input device elements can be optimized by the criterion of maximum broadband, since the error from its own consumption is taken into account;- one low-level shunt, which reduces the cost of the wattmeter; is used in the scheme- additive errors of the voltage conversion channel are minimized.

Analysis of Properties of an Active Linear Gesture Sensor

Abstract Basic gesture sensors can play a significant role as input units in mobile smart devices. However, they have to handle a wide variety of gestures while preserving the advantages of basic sensors. In this paper a user-determined approach to the design of a sparse optical gesture sensor is proposed. The statistical research on a study group of individuals includes the measurement of user-related parameters like the speed of a performed swipe (dynamic gesture) and the morphology of fingers. The obtained results, as well as other a priori requirements for an optical gesture sensor were further used in the design process. Several properties were examined using simulations or experimental verification. It was shown that the designed optical gesture sensor provides accurate localization of fingers, and recognizes a set of static and dynamic hand gestures using a relatively low level of power consumption.

On Growing Operating Costs in the Armed Forces

Operating costs per unit of input or output are often claimed to grow faster in defence than elsewhere in the economy. In this paper, we outline several possible reasons as to why operating costs increase at higher rates in defence, including differences in the input factor mix and productivity growth, the technological complexity required to maintain the relative effect of weapon systems and a gradual reduction in the number of units and activity. We investigate whether operating costs grow at a faster rate than elsewhere in the economy, by estimating growth in real output unit costs in the Norwegian Armed Forces for the period 1994–2013, using activity as a measure of output. We find no increase beyond general inflation in structural (fixed) costs, whereas activity-based (variable) costs per unit of activity increase significantly.

Agroecosystem energy transitions in the old and new worlds: trajectories and determinants at the regional scale

Energy efficiency in biomass production is a major challenge for a future transition to sustainable food and energy provision. This study uses methodologically consistent data on agroecosystem energy flows and different metrics of energetic efficiency from seven regional case studies in North America (USA and Canada) and Europe (Spain and Austria) to investigate energy transitions in Western agroecosystems from the late nineteenth to the late twentieth centuries. We quantify indicators such as external final energy return on investment (EFEROI, i.e., final produce per unit of external energy input), internal final EROI (IFEROI, final produce per unit of biomass reused locally), and final EROI (FEROI, final produce per unit of total inputs consumed). The transition is characterized by increasing final produce accompanied by increasing external energy inputs and stable local biomass reused. External inputs did not replace internal biomass reinvestments, but added to them. The results were declining EFEROI, stable or increasing IFEROI, and diverging trends in FEROI. The factors shaping agroecosystem energy profiles changed in the course of the transition: Under advanced organic and frontier agriculture of the late nineteenth and early twentieth centuries, population density and biogeographic conditions explained both agroecosystem productivity and energy inputs. In industrialized agroecosystems, biogeographic conditions and specific socio-economic factors influenced trends towards increased agroecosystem specialization. The share of livestock products in a region’s final produce was the most important factor determining energy returns on investment.

Evolutionary multi-objective optimization assisted by metamodels, kernel PCA and multi-criteria decision making techniques with applications in aerodynamics

This paper presents two methods which aim at improving the efficiency of evolutionary algorithms (EAs) and metamodel-assisted EAs (MAEAs) used to solve multi-objective optimization problems with computationally expensive evaluation tools. The EAs and MAEAs are accelerated by implementing the kernel principal component analysis (KPCA) during: (a) the application of the evolution operators, by processing the population members in a new/feature, rather than the standard design, space and/or (b) the metamodel training, to reduce the number of input units and, thus, get more accurate predictions. Over and above, a variant of EA (or MAEA) which takes the decision maker's (DM) preferences into consideration during the evolution is proposed. In contrast to standard multi-objective EAs which may insufficiently populate the preferred area(s) of the objective space, more non-dominated solutions are now driven towards them. This is achieved by using the multi-criteria decision making (MCDM) Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), which affects the parent selection and the non-dominated front trimming operators. The combined use of KPCA and TOPSIS is implemented, too. The proposed methods are evaluated by solving two computationally demanding aerodynamic shape optimization problems, with two objectives each.

A discourse-aware neural network-based text model for document-level text classification

Capturing semantics scattered across entire text is one of the important issues for Natural Language Processing (NLP) tasks. It would be particularly critical with long text embodying a flow of themes. This article proposes a new text modelling method that can handle thematic flows of text with Deep Neural Networks (DNNs) in such a way that discourse information and distributed representations of text are incorporate. Unlike previous DNN-based document models, the proposed model enables discourse-aware analysis of text and composition of sentence-level distributed representations guided by the discourse structure. More specifically, our method identifies Elementary Discourse Units (EDUs) and their discourse relations in a given document by applying Rhetorical Structure Theory (RST)-based discourse analysis. The result is fed into a tree-structured neural network that reflects the discourse information including the structure of the document and the discourse roles and relation types. We evaluate the document model for two document-level text classification tasks, sentiment analysis and sarcasm detection, with comparisons against the reference systems that also utilise discourse information. In addition, we conduct additional experiments to evaluate the impact of neural network types and adopted discourse factors on modelling documents vis-à-vis the two classification tasks. Furthermore, we investigate the effects of various learning methods, input units on the quality of the proposed discourse-aware document model.

Determinants of bus rapid transit (BRT) system revenue and effectiveness – A global benchmarking exercise

Bus rapid transit systems (BRT) have evolved in all shapes and sizes around the world in the last 30years motivated by providing greater efficiency and value for money than potential alternatives. This paper aims to explore and compare the effectiveness (including its determinants) and revenue potential of 58 BRT systems globally. A key research question for this paper is to what extent there is a trade-off between long term capital expenditure and short term operating cost. The results suggest that BRT systems located in developing countries or countries that have high population densities are successful in generating higher revenues per passenger and unit of input than their conventional bus counterparts but are from a community perspective not more cost effective in doing so. Better BRT standards and hence higher capital expenditure, while significantly increasing patronage and input effectiveness do not have a significant impact on either yields or cost effectiveness. In contrast, public ownership and the number of stations are on average associated with higher cost effectiveness scores.

Basic emotions and adaptation. A computational and evolutionary model.

The core principles of the evolutionary theories of emotions declare that affective states represent crucial drives for action selection in the environment and regulated the behavior and adaptation of natural agents in ancestrally recurrent situations. While many different studies used autonomous artificial agents to simulate emotional responses and the way these patterns can affect decision-making, few are the approaches that tried to analyze the evolutionary emergence of affective behaviors directly from the specific adaptive problems posed by the ancestral environment. A model of the evolution of affective behaviors is presented using simulated artificial agents equipped with neural networks and physically inspired on the architecture of the iCub humanoid robot. We use genetic algorithms to train populations of virtual robots across generations, and investigate the spontaneous emergence of basic emotional behaviors in different experimental conditions. In particular, we focus on studying the emotion of fear, therefore the environment explored by the artificial agents can contain stimuli that are safe or dangerous to pick. The simulated task is based on classical conditioning and the agents are asked to learn a strategy to recognize whether the environment is safe or represents a threat to their lives and select the correct action to perform in absence of any visual cues. The simulated agents have special input units in their neural structure whose activation keep track of their actual “sensations” based on the outcome of past behavior. We train five different neural network architectures and then test the best ranked individuals comparing their performances and analyzing the unit activations in each individual’s life cycle. We show that the agents, regardless of the presence of recurrent connections, spontaneously evolved the ability to cope with potentially dangerous environment by collecting information about the environment and then switching their behavior to a genetically selected pattern in order to maximize the possible reward. We also prove the determinant presence of an internal time perception unit for the robots to achieve the highest performance and survivability across all conditions.

Application of generalized regression neural network on the prediction of the main girths of young females based on 3D point cloud data

The widths and thicknesses of 110 female undergraduates are obtained from the extracted silhouette, the girths are measured by using software Imageware based on 3D point cloud data, which corresponding to input and output units of the optimized generalized regression neural network (GRNN) and independent and dependent variables of the multiple linear regression (MLR) model. The training and test samples are used to establish and test the validity of GRNN and MLR model. The result shows that the GRNN is superior to the MLR.

Application of DEA and statistical inference to model the determinants of biomethane production efficiency: A case study in south China

Global interest in the conversion of organic biowaste to biomethane is increasing rapidly. As new projects are built, managers must ensure that biomethane engineering processes are operating efficiently. In China, increasing biogas energy output is an integral part of the central government’s 13th Five Year Plan. However, many biogas plants that convert various organic waste types to energy in China operate inefficiently. In this context, the objective of this research is to investigate the determinants of efficiency in a major biogas engineering pilot project that converts municipal, industrial and agricultural waste into biomethane vehicle fuel. The methodology involved applying data envelopment analysis and multiple linear regression to determine factors statistically significant for fluctuations in performance efficiency. The results provided important insights. First, variables statistically significant for the production of primary outputs were isolated. The variables most influential for biomethane production included bagasse input, fish waste input, and cassava input. As for solid fertilizer output, the most influential variables included manure input, fish waste, other input, and FeCl2. Secondly, the surveyed case was found to have significant scale inefficiencies, which has important implications for optimization of industrial scale co-digestion projects. As time progressed, the project experienced decreasing returns to scale, indicating that although overall inputs increased, production per unit of input decreased. Third, specific input/output targets and slacks were computed in order to identify changes required for the project to become efficient at certain points in time over the survey period. Fourth, possible determinants of efficiency were analyzed. The paper concludes with several engineering management and policy suggestions to enhance biomethane conversion efficiency.

Are baboons learning "orthographic" representations? Probably not.

The ability of Baboons (papio papio) to distinguish between English words and nonwords has been modeled using a deep learning convolutional network model that simulates a ventral pathway in which lexical representations of different granularity develop. However, given that pigeons (columba livia), whose brain morphology is drastically different, can also be trained to distinguish between English words and nonwords, it appears that a less species-specific learning algorithm may be required to explain this behavior. Accordingly, we examined whether the learning model of Rescorla and Wagner, which has proved to be amazingly fruitful in understanding animal and human learning could account for these data. We show that a discrimination learning network using gradient orientation features as input units and word and nonword units as outputs succeeds in predicting baboon lexical decision behavior—including key lexical similarity effects and the ups and downs in accuracy as learning unfolds—with surprising precision. The models performance, in which words are not explicitly represented, is remarkable because it is usually assumed that lexicality decisions, including the decisions made by baboons and pigeons, are mediated by explicit lexical representations. By contrast, our results suggest that in learning to perform lexical decision tasks, baboons and pigeons do not construct a hierarchy of lexical units. Rather, they make optimal use of low-level information obtained through the massively parallel processing of gradient orientation features. Accordingly, we suggest that reading in humans first involves initially learning a high-level system building on letter representations acquired from explicit instruction in literacy, which is then integrated into a conventionalized oral communication system, and that like the latter, fluent reading involves the massively parallel processing of the low-level features encoding semantic contrasts.

Exergy analysis of Chinese agriculture

• An in-depth exergy analysis of Chinese agriculture during 2001–2015 is carried out. • Exergy-based indicators are devised to assess resource and environmental performance. • Chinese agriculture was increasingly dependent on purchased non-renewable resources. • The improvement of resource use efficiency and total productivity were identified. • Exergy analysis proved a reliable tool to pinpoint the sustainability of agriculture. Along with the growing population scale and improving diet structure, a great challenge has been faced to reconcile food supply, resource use and environmental impact in contemporary agriculture. This paper presents an in-depth exergy analysis of Chinese agriculture covering cropping, forest, stockbreeding and fishery over the period of 2001–2015. The resource, environmental and economic performance of the agricultural system are illustrated by accounting all the major exergetic fluxes, which include free nature resources input, purchased economic investments, waste emissions or virtual environmental inputs, and harvested yields of agricultural products. Chinese agriculture has experienced accelerating transition from a self-supporting mode to a modern energy-intensive pattern, which is characterized by lower renewability index, higher non-renewable resource consumption and economic investment ratio, and larger waste emissions and environmental resource input. The improvements of resource use efficiency and total agricultural productivity are also identified as indicated by the evolution of resource yield ratio and system transformity. In 2015, as to 100 units of agricultural yield, there were about 109 units of purchased resource input including 18 units purchased nonrenewable resources, 748 units of free natural resource input and 21 units of environmental impact. Exergy analyses capture the resource and environmental performance of agricultural ecological economic system by pinpointing the real exergy dissipation and cost processes, which could help policy makers to couple biophysical concepts more robustly for improving resource use efficiency and achieving sustainable development of Chinese agriculture in the 21 st century.

Learning explicit video attributes from mid-level representation for video captioning

Recent works on video captioning mainly learn the map from low-level visual features to language description directly without explicitly representing the high-level semantic video concepts (e.g. objects, actions in the annotated sentences). To bridge the semantic gap, in this paper, addressing it, we propose a novel video attribute representation learning algorithm for video concept understanding and utilize the learned explicit video attribute representation to improve video captioning performance. To achieve it, firstly, inspired by the success of spectrogram in audio processing, a novel mid-level video representation named “video response map” (VRM) is proposed, by which the frame sequence could be represented by a single image representation. Therefore, the video attributes representation learning could be converted to a well-studied multi-label image classification problem. Then in the captions prediction step, the learned video attributes feature is integrated with the single frame feature to improve previous sequence-to-sequence language generation model by adjusting the LSTM (Long-Short Term Memory) input units. The proposed video captioning framework could both handle variable frame inputs and utilize high-level semantic video attribute features. Experimental results on video captioning tasks show that the proposed method, utilizing only RGB frames as input without extra video or text training data, could achieve competitive performance with state-of-the-art methods. Furthermore, the extensive experimental evaluations on the UCF-101 action classification benchmark well demonstrate the representation capability of the proposed VRM.

DEVELOPMENT OF MICROCONTROLLER BASED BINAURAL DIGITAL HEARING AIDS FOR HEARING-IMPAIRED PEOPLE

This research paper expounds microcontroller based binaural digital hearing aids for hearing-impaired people by making use of ATmega328 microcontroller and other circuitries to process the audio signal input by either increasing or reducing the gain level of input audio signal, filter background noise, frequencies compression, save battery power and minimize circuit by making use of the internal ADC of the microcontroller and two PMW pins of the microcontroller as DAC. Hearing impairment among the youths and adults nowadays are in the increase, due wrong use of phones of which every minute of the day someone’s earphone is on listening to one type of music or the other. In other to solve the problem created so to say this research work was conceived and given birth to. The different stages of digital hearing aid are designed and then simulated first in Proteus software which then was implemented using PCB-board. The main components of this system were the audio input unit which consists of the microphone and its pre-amplifier, the microcontroller (ATmega328) which consists of the ADC, the DAC and the audio signal processing, the filter stage and control codes (frequencies compression codes, power saver codes, acoustic feedback control codes, signal level control and adaptive adjustment codes etc.), the power amplifier and volume control unit and then the earphones (output). The control codes were written in C language while Ardinuo Uno compiler was used to write the codes into ATmega328. The prototype has an overall system gain of 27dB and the power output of 32.5mW. The prototype was tested with a patient that has a hearing impairment and the patient was satisfactory with the device. http://dx.doi.org/10.4314/njt.v36i3.34

DEVELOPMENT OF MICROCONTROLLER BASED BINAURAL DIGITAL HEARING AIDS FOR HEARING-IMPAIRED PEOPLE

This research paper expounds microcontroller based binaural digital hearing aids for hearing-impaired people by making use of ATmega328 microcontroller and other circuitries to process the audio signal input by either increasing or reducing the gain level of input audio signal, filter background noise, frequencies compression, save battery power and minimize circuit by making use of the internal ADC of the microcontroller and two PMW pins of the microcontroller as DAC. Hearing impairment among the youths and adults nowadays are in the increase, due wrong use of phones of which every minute of the day someone’s earphone is on listening to one type of music or the other. In other to solve the problem created so to say this research work was conceived and given birth to. The different stages of digital hearing aid are designed and then simulated first in Proteus software which then was implemented using PCB-board. The main components of this system were the audio input unit which consists of the microphone and its pre-amplifier, the microcontroller (ATmega328) which consists of the ADC, the DAC and the audio signal processing, the filter stage and control codes (frequencies compression codes, power saver codes, acoustic feedback control codes, signal level control and adaptive adjustment codes etc.), the power amplifier and volume control unit and then the earphones (output). The control codes were written in C language while Ardinuo Uno compiler was used to write the codes into ATmega328. The prototype has an overall system gain of 27dB and the power output of 32.5mW. The prototype was tested with a patient that has a hearing impairment and the patient was satisfactory with the device. http://dx.doi.org/10.4314/njt.v36i3.34