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A Study on New Control Mechanisms of Memory
Haibin LIU,Yukinori KAKAZU 대한전자공학회 1992 대한전자공학회 학술대회 Vol.1992 No.10
A physical phenomenon is observed through analysis of the Hodgkin-Huxley's model that is, according to Maxwell field equations a fired neuron can yield magnetic fields. The magnetic signals are an output of the neuron as some type of information, which may be supposed to be the conscious control information. Therefore, study on neural networks should take the field effect into consideration. Accordingly, a study on the behavior of a unit neuron in the field is made and a new neuron model is proposed. A mathematical Memory-Learning Relation has been derived from these new neuron equations, some concepts of memory and learning are introduced. Two learning theorems are put forward, and the control mechanisms of memory are also discussed. Finally, a theory, i.e. Neural Electromag metic(NEM) field theory is advanced.
Kinetic modeling for chromatographic separation of cytosine monophosphate and uracil monophosphate
Haibin Qu,Yong Chen,Weixing Dai,Xuesong Liu,Yiyu Cheng 한국화학공학회 2006 Korean Journal of Chemical Engineering Vol.23 No.5
pharmaceutical industries. In this study, chromatographic separation of the two nucleotides CMP and UMP was sim-ulated by the equilibrium-dispersive (ED) model, and the adsorption isotherms in the ED model were determined bythe inverse method. Prediction performance of the model was validated under three diferent kinds of conditions andthe importance of selecting isotherms was discussed in detail. Excellent agreement was achieved with the experi-mental band profiles and the prediction of the ED model. The ED model with bi-Langmuir isotherm was especiallysuitable for simulating chromatographic separation of CMP and UMP. The error of prediction by the ED model with
Liu Dongmei,Ouyang Haibin,Li Steven,장춘량,Zhan Zhi-Hui 한국CDE학회 2023 Journal of computational design and engineering Vol.10 No.4
Because of the good performance of convolutional neural network (CNN), it has been extensively used in many fields, such as image, speech, text, etc. However, it is easily affected by hyperparameters. How to effectively configure hyperparameters at a reasonable time to improve the performance of CNNs has always been a complex problem. To solve this problem, this paper proposes a method to automatically optimize CNN hyperparameters based on the local autonomous competitive harmony search (LACHS) algorithm. To avoid the influence of complicated parameter adjustment of LACHS algorithm on its performance, a parameter dynamic adjustment strategy is adopted, which makes the pitch adjustment probability PAR and step factor BW dynamically adjust according to the actual situation. To strengthen the fine search of neighborhood space and reduce the possibility of falling into local optima for a long time, an autonomous decision-making search strategy based on the optimal state is designed. To help the algorithm jump out of the local fitting situation, this paper proposes a local competition mechanism to make the new sound competes with the worst harmonic progression of local selection. In addition, an evaluation function is proposed, which integrates the training times and recognition accuracy. To achieve the purpose of saving the calculation cost without affecting the search result, it makes the training time for each model depending on the learning rate and batch size. In order to prove the feasibility of LACHS algorithm in configuring CNN superparameters, the classification of the Fashion-MNIST dataset and CIFAR10 dataset is tested. The comparison is made between CNN based on empirical configuration and CNN based on classical algorithms to optimize hyperparameters automatically. The results show that the performance of CNN based on the LACHS algorithm has been improved effectively, so this algorithm has certain advantages in hyperparametric optimization. In addition, this paper applies the LACHS algorithm to expression recognition. Experiments show that the performance of CNN optimized based on the LACHS algorithm is better than that of the same type of artificially designed CNN. Therefore, the method proposed in this paper is feasible in practical application.
Song Jinling,Huang Liming,Gao Yan,Liu Aiyong,Liu Haibin 보안공학연구지원센터 2016 International Journal of u- and e- Service, Scienc Vol.9 No.5
To ameliorate the limitations of traditional collaborative filtering technologies and enhance the recommendation quality of agricultural science and technology information, a collaborative filtering recommendation method based on synthetic strategy is proposed. Firstly, filter the user set and user-item rating matrix according to the location of target user, which can solve the regional problem. Then, predict ratings of items according to the similarity of users or item content, which can relax the impact of the sparse rating. In addition, add the rating time to the user-item rating matrix to distinguish the timeliness of the user preference, and add user preference shifting in the similarity formula as a factor which can express the similarity of users or item content better. Our method can not only guarantee the recommended information is local and suit to current season of agricultural production, but also ensure the recommending precision under sparse rating data.
1인승 태양광 자동차의 전기 장치 효율과 재원이 주행에 끼치는 영향에 관한 연구
정연수(Yeon-soo Jeong),하양(Yang He),유해빈(Haibin Liu),이승현(Seung-hyun Lee),김철호(Chul-Ho Kim) 한국자동차공학회 2014 한국자동차공학회 학술대회 및 전시회 Vol.2014 No.11
As the environmental pollution becomes global problem, the importance of eco-friendly cars is magnified. Especially, the solar car has high potential caused that many solar car races are being held around the world. In this paper, the various factors of the solar car components are analyzed by using power consumption model to infer that how factors have an effect on the driving in the Australian environment which is the place being held WSC. Based on the analysis, motor and solar cell efficiencies have a most impact on running performance.