Deep Global Multiple-Scale and Local Patches Attention Dual-Branch Network for Pose-Invariant Facial Expression Recognition

Pose-invariant facial expression recognition(FER)is an active but challenging research topic in computer vision.Especially with the involvement of diverse observation angles,FER makes the training parameter models inconsistent from one view to another.This study develops a deep global multiple-scale and local patches attention(GMS-LPA)dual-branch network for pose-invariant FER to weaken the influence of pose variation and selfocclusion on recognition accuracy.In this research,the designed GMS-LPA network contains four main parts,i.e.,the feature extraction module,the global multiple-scale(GMS)module,the local patches attention(LPA)module,and the model-level fusion model.The feature extraction module is designed to extract and normalize texture information to the same size.The GMS model can extract deep global features with different receptive fields,releasing the sensitivity of deeper convolution layers to pose-variant and self-occlusion.The LPA module is built to force the network to focus on local salient features,which can lower the effect of pose variation and self-occlusion on recognition results.Subsequently,the extracted features are fused with a model-level strategy to improve recognition accuracy.Extensive experimentswere conducted on four public databases,and the recognition results demonstrated the feasibility and validity of the proposed methods.

Development of the precision feeding system for sows via a rule-based expert system

To precisely meet the nutritional requirements of sows during the stages of pregnancy and lactation,a precision feeding system was developed by using the intelligent sow feeder combined with a rule-based expert system and the Internet of Things(IoTs).The model of uncertain knowledge representation was established for inference by using the certainty factor.The daily feeding amount of each sow was calculated by the expert system.An improved pattern matching algorithm Reused Degree Model-RETE(RDM-RETE)was proposed for the decision of daily feeding amount,which sped up inference by optimizing the RETE network topology.A prediction model of daily feeding amount was established by a rule-based expert system and the precision feeding was achieved by an accurate control technology of variable volume.The experimental results demonstrated that the HASH-RDM-RETE algorithm could effectively reduce the network complexity and improve the inference efficiency.The feeding amount decided by the expert system was a logarithmic model,which was consistent with the feeding law of lactating sows.The inferential feeding amount was adopted as the predicted feed intake and the coefficient of correlation between predicted feed intake and actual feed intake was greater than or equal to 0.99.Each sow was fed at different feeding intervals and different feed amounts for each meal in a day.The feed intake was 26.84% higher than that of artificial feeding during lactation days(p<0.05).The piglets weaned per sow per year(PSY)can be increased by 1.51 compared with that of relatively high levels in domestic pig farms.This system is stable in feeding and lowers the breeding cost that can be applied in precision feeding in swine production.

Assessment of the environmental comfort of lactating sows via improved analytic hierarchy process and fuzzy comprehensive evaluation

Since there are many interacting influence factors of the comfortable degree of lactating sows,a method that combines improved analytic hierarchy process(IAHP)and fuzzy comprehensive evaluation(FCE)was introduced to conduct a quantitative evaluation of the comfortable degree.Besides,an evaluation index system was established,and the weights of different indicators were determined by using IAHP method,including temperature,relative humidity,concentrations of carbon dioxide(CO_(2)),ammonia(NH_(3)),hydrogen sulfide(H_(2)S),and air speed.The construction method of fuzzy membership function and the calculation method of the parameters were proposed following the principle that the summation of membership degrees is equal to 1.Three basic types of membership functions(MFs),i.e.,ridgemf,gaussmf,and trimf were used to build an evaluation model which fitted IAHP-FCE well.The proposed method was verified and applied based on the environmental data in different seasons obtained from a pig farm in Zhenjiang City,Jiangsu Province,China.It is demonstrated that the proposed IAHP-FCE model with various types of MFs has drawn a unique and consistent conclusion.Moreover,the IAHP-FCE model has a higher correlation coefficient of 0.874 compared with the single-factor evaluation(SFE)model.The IAHP-FCE model could be served as a beneficial strategy for the precise regulation and early warning of environmental conditions to improve sow welfare.

Automatic cruise system for water quality monitoring

The range of unit fixed-point measurement on water quality monitoring system is limited,and the cost for multipoint measurement is high.In order to solve these problems,the automatic cruise system for water quality monitoring was designed.Sage-Husa adaptive Kalman filtering algorithm was adopted to correct the error in GPS positioning.The boat was equipped with ship control module,water quality parameters acquisition module,power-supply module,GPS module and GPRS-DTU packet data transmission module.An Android application was developed so that individual users can use smartphone to communicate with the boat at all time and places.The results show that the boat can basically cruise in the set route to monitor the water quality.In a 4 m^(2) aquatic plants areas,the dissolved oxygen monitored in different time were about 10.2%,8.5%and 8.3%,respectively,higher than other areas,and the pH values were 4.1%,3.8%and 3.7%higher than those in other waters,which shown that plants photosynthesis released oxygen consumption of carbon dioxide will affect the dissolved oxygen content and pH value.This system can widen the measurement range,and lower the measuring cost that can be widely used in the water quality monitoring in aquaculture and river management.