Non-destructive sensing for determining Sunagoke moss water content -bio-inspired approaches-

One of the primary determinants of Sunagoke moss Rachomitrium japonicum growth is water availability. There is need to develop non-destructive sensing of Sunagoke moss water content to realize automation and precision irrigation in a close bio-production system. Machine vision can be utilized as non-destructive sensing to recognize changes in some kind of features that describe the water conditions from the appearance of wilting Sunagoke moss. The goal of this study is to propose and investigate bio-inspired algorithms i.e. neural-genetic algorithms (neural-GAs) and neural-ant colony optimization (neural-ACO) to find the most significant set of image features suitable for predicting cultured Sunagoke moss water content in a close bio-production system. Features extracted consisted of 13 colour features, 90 textural features (grey level co-occurrence matrix, RGB, HSV and HSL colour co-occurrence matrix textural features) and three morphological features. Each colour space consisted of ten textural features algorithms: entropy, energy, contrast, homogeneity, sum mean, variance, correlation, maximum probability, inverse difference moment and cluster tendency. The specificity of this problem was that we were not looking for single image feature but several associations of image features that may be involved in determining water content of Sunagoke moss. Neural-ACO had better prediction performance with lower number of features than neural-GAs. The minimum validation prediction mean square error (MSE) achieved was 2.02x10-3 when using 10 relevant features

Daily Worker Evaluation Model for SME-scale Food Production System Using Kansei Engineering and Artificial Neural Network

AbstractThis paper highlighted a daily worker evaluation model for small medium-scale food production system. The model consist of worker capacity assessment and worker performance evaluation sub-models. The model measures the relationship between Total Mood Disturbance (TMD), heart rate of worker and workplace parameters using Kansei Engineering approach.However, the rapid measurement of TMD is difficult and full of bias since using the paper-based questionnaire of Profile of Mood States (POMS). Therefore, a rapid measurement method was developed using Artificial Neural Network to support the application of daily evaluation model. The inputs of the model were heart rate, workplace temperature, relative humidity, light intensity and noise level, which were measured before and after working. The output was TMD score.The training and inspection data for ANN was collected from workers of food production system as Tempe, Bakpia, Fish Chips and Crackers industries in Yogyakarta Special Region.ANN model were tested successfully predicted TMD score using back-propagation supervised learning method. The trained ANN model generated satisfied root mean square error value. ANN model is possible to substitute conventional data acquisition of POMS. The daily evaluation model is applicable to assist industrial management for providing the appropriate worker assignment for shift schedulling and environmental set point for the workplace comfortability

Combining Kansei Engineering and Artificial Neural Network to Assess Worker Capacity in Small-Medium Food Industry

This paper highlighted a new method for worker capacity assessment in Indonesian small-medium food industry. The sustainable and productivity of Indonesian food industry should be maintained based on the workers capacity. The status of worker capacity could be categorized as normal, capacity constrained worker and bottleneck. By using Kansei Engineering, worker capacity can be assessed using verbal response of profile of mood states, non-verbal response of heart rate in a given workplace environmental parameters. Fusing various Kansei Engineering parameters of worker capacity requires a robust modeling tool. Artificial Neural Network (ANN) is required to assess worker capacity. The model was demonstrated via a case study of Tempe Industry. The trained ANN model generated satisfied accuracy and minimum error. The research results concluded the possibility to assess worker capacity in Indonesian small-medium food industry by combining Kansei Engineering and ANN

Combining Kansei Engineering and Artificial Neural Network to Assess Worker Capacity in Small-Medium Food Industry

This paper highlighted a new method for worker capacity assessment in Indonesian small-medium food industry. The sustainable and productivity of Indonesian food industry should be maintained based on the workers capacity. The status of worker capacity could be categorized as normal, capacity constrained worker and bottleneck. By using Kansei Engineering, worker capacity can be assessed using verbal response of profile of mood states, non-verbal response of heart rate in a given workplace environmental parameters. Fusing various Kansei Engineering parameters of worker capacity requires a robust modeling tool. Artificial Neural Network (ANN) is required to assess worker capacity. The model was demonstrated via a case study of Tempe Industry. The trained ANN model generated satisfied accuracy and minimum error. The research results concluded the possibility to assess worker capacity in Indonesian small-medium food industry by combining Kansei Engineering and ANN

Practical Application of Methanol-Mediated Mutualistic Symbiosis between Methylobacterium Species and a Roof Greening Moss, Racomitrium japonicum

Bryophytes, or mosses, are considered the most maintenance-free materials for roof greening. Racomitrium species are most often used due to their high tolerance to desiccation. Because they grow slowly, a technology for forcing their growth is desired. We succeeded in the efficient production of R. japonicum in liquid culture. The structure of the microbial community is crucial to stabilize the culture. A culture-independent technique revealed that the cultures contain methylotrophic bacteria. Using yeast cells that fluoresce in the presence of methanol, methanol emission from the moss was confirmed, suggesting that it is an important carbon and energy source for the bacteria. We isolated Methylobacterium species from the liquid culture and studied their characteristics. The isolates were able to strongly promote the growth of some mosses including R. japonicum and seed plants, but the plant-microbe combination was important, since growth promotion was not uniform across species. One of the isolates, strain 22A, was cultivated with R. japonicum in liquid culture and in a field experiment, resulting in strong growth promotion. Mutualistic symbiosis can thus be utilized for industrial moss production

Modeling Preference Reasoning for Customizable Biological Greening Material using Bayesian Belief Network and Particle Swarm Optimization

This paper presents work on modeling preference reasoning regarding the uses of Customizable Biological Greening Material (CBGM). CBGM is a biological material which is produced to fulfill preference for greening technology in a given consumer segment. The objectives of the paper were: 1) To propose modeling preference reasoning for CBGM by predicting its attributes importance using consumer mentality constraints; 2) To develop the modeling by hybridizing Bayesian Belief Network (BBN) model and Particle Swarm Optimization (PSO).  These attributes are used as information sources to support decision for producing biological material in plant factory and applying its functionality in the greening technology. The inputs of modeling were various consumer mentality constraints as different demographic, their prior knowledge, familiarity, agreement to material function and interest. The output was a predicted attribute importance of a preferred material. BBN and PSO were hybridized to take advantage of both methods to identify the probability-based reasoning and maximize the satisfaction using the analogy between the consumer preference and social behavior of animal swarm. The modeling was demonstrated on a case study of moss material (Rhacomitrium canescens). The materials were offered to the respondents using questionnaires we designed. A 24 simple BBN model was used to predict each attribute importance. PSO was used to optimize a 24 simple BBN model using a satisfaction function. Hybrid modeling of BBN and PSO has indicated the performance improvement of reasoning model compared to single modeling of BBN. The improvement was based on satisfied correlation and minimum error between measured and predicted value. It was concluded that consumer mentality constraints are possible to be used as inputs to predict an attribute importance of the preferred moss material. Subsequently, hybrid modeling of BBN and PSO is a feasible method to model reasoning easily and accurately. The modeling and information in this paper are applicable to expand the application of greening technology in different consumer segments and different contexts.  Keywords:   Attribute importance, Bayesian belief network, biological material for greening technology, mentality constraint, particle swarm optimization