Behavioural pattern identification and prediction in intelligent environments

In this paper, the application of soft computing techniques in prediction of an occupant's behaviour in an inhabited intelligent environment is addressed. In this research, daily activities of elderly people who live in their own homes suffering from dementia are studied. Occupancy sensors are used to extract the movement patterns of the occupant. The occupancy data is then converted into temporal sequences of activities which are eventually used to predict the occupant behaviour. To build the prediction model, different dynamic recurrent neural networks are investigated. Recurrent neural networks have shown a great ability in finding the temporal relationships of input patterns. The experimental results show that non-linear autoregressive network with exogenous inputs model correctly extracts the long term prediction patterns of the occupant and outperformed the Elman network. The results presented here are validated using data generated from a simulator and real environments

Agility index evaluation using fuzzy logic in a supply chain management company

Agility is one of the coming challenges to the supply chain management companies. This paper presents a model to evaluate agility for supply chain management companies and answers the question that how agile a supply chain management company is. It shows a complete set of items in evaluating agility in SCM companies.The model surveys agility in responsiveness with indexes such as strategic planning, sensitivity to change and virtual enterprise; flexibility with indexes such as market flexibility, logistics flexibility,operations system flexibility and supply flexibility;competency with indexes such as integrative mechanism, shared culture, joint decision making, trust and communication; and finally quickness with indexes such as speed of new product introduction,delivery time and speed in operation.The results of S.G.S Co. show that it is in the middle range of agility. It also identifies weak factors within the organization which by improving them, the company can improve its agility index. Evaluation is done in fuzzy logic

Echo state network for occupancy prediction and pattern mining in intelligent environment

Pattern analysis and prediction of sensory data is becoming an increasing scientific challenge and a massive economical interest supports the need for better pattern mining techniques. The aim of this paper is to investigate efficient mining of useful information from a sensor network representing an ambient intelligence environment. The goal is to extract and predict behavioral patterns of a person in his/her daily activities by analyzing the time series data representing the behaviour of the occupant, generated using occupancy sensors. There are various techniques available for analysis and prediction of a continuous time series signal. However, the occupancy signal is represented by a binary time series where only discrete values of a signal are available. To build the prediction model, recurrent neural networks are investigated. They are proven to be useful tools to solve the difficulties of the temporal relationships of inputs between observations at different time steps, by maintaining internal states that have memory. In this paper, a special form of recurrent neural network, the so-called Echo State Network (ESN) is used in which discrete values of time series can be well processed. Then, a model developed based on ESN is compared with the most popular recurrent neural net-works; namely Back Propagation Through Time (BPTT) and Real Time Recurrent Learning (RTRL). The results showed that ESN provides better prediction results compared with BPTT and RTRL. Using ESN, large datasets are learnt in only few minutes or even seconds. It can be concluded that ESN are efficient and valuable tools in binary time series prediction. The results presented in this paper are based on simulated data generated from a simulator representing a person in a 1 bedroom flat

User activities outliers detection; integration of statistical and computational intelligence techniques

In this paper, a hybrid technique for user activities outliers detection is introduced. The hybrid technique consists of a two-stage integration of Principal Component Analysis (PCA) and Fuzzy Rule-Based Systems (FRBS). In the first stage, the Hamming distance is used to measure the differences between different activities. PCA is then applied to the distance measures to find two indices of Hotelling's T2 and Squared Prediction Error. In the second stage of the process, the calculated indices are provided as inputs to the FRBSs to model them heuristically. The model is used to identify the outliers and classify them. The proposed system is tested in real home environments, equipped with appropriate sensory devices, to identify outliers in the activities of daily living of the user. Three case studies are reported to demonstrate the effectiveness of the proposed system. The proposed system successfully identifies the outliers in activities distinguishing between the normal and abnormal behavioural patterns

Agility index evaluation using fuzzy logic in a supply chain management company

Agility is one of the coming challenges to the supply chain management companies. This paper presents a model to evaluate agility for supply chain management companies and answers the question that how agile a supply chain management company is. It shows a complete set of items in evaluating agility in SCM companies.The model surveys agility in responsiveness with indexes such as strategic planning, sensitivity to change and virtual enterprise; flexibility with indexes such as market flexibility, logistics flexibility,operations system flexibility and supply flexibility;competency with indexes such as integrative mechanism, shared culture, joint decision making, trust and communication; and finally quickness with indexes such as speed of new product introduction,delivery time and speed in operation.The results of S.G.S Co. show that it is in the middle range of agility. It also identifies weak factors within the organization which by improving them, the company can improve its agility index. Evaluation is done in fuzzy logic

Gamma irradiation effects on physical properties of squash seeds

In order to study the effects of gamma radiation on some physical properties of squash (Cucurbit pepo. L) seed, five irradiation doses (25, 50, 75, 100 and 200 GY) have been used.  Some physical properties, including dimensional properties (length, width, thickness, geometric mean diameter, sphericity, volume, surface area, projected area, flakiness ratio and elongation ratio), mass, 1,000 seeds mass, bulk density, true density and porosity of gamma irradiated squash seeds were measured.  Statistical indices including maximum, minimum, average, variance, skewness and kurtosis, for dimensional properties and mass of the seeds were calculated.  Results revealed a significant raise in hollow seeds number by increasing gamma irradiation dose from 5% for 25 GY to nearly 100% for 100 and 200 GY.  On the other hand, length, width, thickness, mass of single seed, 1,000 seeds mass and porosity showed an increase followed by a decrease with the increasing gamma irradiation dose.  With the increasing gamma irradiation dose, true and bulk densities were found to decrease from 338.41   kg m-3 to 214.01 kg m-3 and 420.16 kg m-3 to 256.12 kg m-3, respectively.  In 100 and 200 GY all seeds were hollow and very small, therefore dimensions and mass of these seeds were not measured. Keywords: gravimetric properties, dimensional properties, squash seeds, irradiation, gamma ra

Effects of storage duration and conditions on mechanical properties of Viola cucumber fruit under compression loading

In this research, the effects of storage duration and storage conditions on mechanical properties of cucumber fruit (Viola variety) were evaluated at different positions of the fruit length.  The cucumber fruit mechanical properties determined in this study were firmness, apparent modulus of elasticity, failure stress, failure strain and failure energy.  The mechanical properties determined under compression loading using puncture and uni-axial compression tests.  The results showed that the storage duration, storage conditions and fruit test position had significant (P<0.01) effect on the mechanical properties of Viola cucumber fruit.  The samples firmness, modulus of elasticity, failure stress and failure energy reduced about 49%, 39%, 38% and 33%, respectively during shelf life.  The failure strain of samples increased 18% during storage time.  Changing the mechanical properties of the cucumber fruit at room conditions was faster than refrigerator conditions.  The mechanical properties were differed along the length of cucumber fruits so that near the stem region of cucumber fruit had the maximum value of firmness, modulus of elasticity, failure stress and failure energy.  The sample failure strain had the minimum value at near the stem region of cucumber fruit.  Among the mechanical parameters that were evaluated in this research work, the firmness can be considered as the most appropriate parameter to evaluate textural properties of Viola cucumber fruit due to significant effect of independent parameters on it and ease of usage

Efficient Pb (II) removal from wastewater by TEG coated Fe3O4 ferrofluid

Tri-ethylene glycol (TEG) coated Fe3O4 nanoparticles ferrofluid were used for Pb (II) removal from simulated wastewater. The samples were synthesized using a modified co-precipitation method. The prepared samples were characterized by different techniques including X-ray diffraction, Rietveld method, FTIR, FESEM, TEM, VSM, TGA, BET and atomic adsorption experiments. The crystallinity of nanoparticles with a cubic spinel ferrite structure and absence of impurity phases were verified using X-ray diffraction and Rietveld method. The presence of TEG was approved by FTIR and thermogravimetric analysis. The VSM results showed that the bonding between the TEG molecules and ferrite nanoparticles, reduces the surface spin disorder, influences the morphology and magnetization, and consequently increases the Pb (II) removal efficiency to a high value of 97%. The obtained high value of adsorption capacity of q=363.4 mg.g-1 with R= 91 % and q=129.4 mg.g-1 with R=97 %shows effective role of TEG coating on Pb (II) adsorption. The interesting results of this study imply that the TEG coated ferrofluid sample is suitable candidate for practical applications