Pub Date : 2019-03-15DOI: 10.33422/3rd.stkconf.2019.03.180
T. Yamasaki, T. Kaburagi, Kaoru Kuramoto, S. Kumagai, T. Matsumoto, Y. Kurihara
{"title":"Development of Unconstrained Respiratory- Arrest Detection System for Use during Sleep Based on Lung-thorax Movement Model","authors":"T. Yamasaki, T. Kaburagi, Kaoru Kuramoto, S. Kumagai, T. Matsumoto, Y. Kurihara","doi":"10.33422/3rd.stkconf.2019.03.180","DOIUrl":"https://doi.org/10.33422/3rd.stkconf.2019.03.180","url":null,"abstract":"","PeriodicalId":119572,"journal":{"name":"Proceedings of 3rd International Conference on Applied Research in Science, Technology and Knowledge","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116647199","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-03-15DOI: 10.33422/3rd.stkconf.2019.03.172
T. Yamasaki, T. Kaburagi, Kaoru Kuramoto, S. Kumagai, T. Matsumoto, Y. Kurihara
{"title":"Pile Displacement Measurement Methodwith High Time Resolution for Rapid Loading Test","authors":"T. Yamasaki, T. Kaburagi, Kaoru Kuramoto, S. Kumagai, T. Matsumoto, Y. Kurihara","doi":"10.33422/3rd.stkconf.2019.03.172","DOIUrl":"https://doi.org/10.33422/3rd.stkconf.2019.03.172","url":null,"abstract":"","PeriodicalId":119572,"journal":{"name":"Proceedings of 3rd International Conference on Applied Research in Science, Technology and Knowledge","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127202961","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-03-15DOI: 10.33422/3rd.stkconf.2019.03.174
T. Yamasaki, T. Kaburagi, Kaoru Kuramoto, S. Kumagai, T. Matsumoto, Y. Kurihara
Urinary tract infections (UTI) are common infections in healthcare facilities and hospitals. Staff members often manage their patients’ urination needs with a catheterization, which relapses the UTI. Therefore, patients need another way for staff to take care of each patients’ urination without catheterization, though this increases the staff’s work burden. If staff could predict how the urine volume accumulates in the bladder in advance, a care schedule could be made easily; and can reduce staff’s work burden. Therefore, we propose a method for predicting the volume of accumulated urine utilizing data measured by an ultrasonic sensor. The proposed method is based on a deterministic model as a differential equation that represents the dynamics of the urine volume accumulation in the bladder, and utilizes the multi-task Gaussian process technique in the model. The Gaussian process predicts urinary volume at each moment by learning patients’ individual characteristics on accumulative transition from the training data on people are mixed. To evaluate the accuracy of the proposed method, we carried out a validity experiment with six subjects. In the experiment, we asked the subject to wear the ultrasonic sensor to measure urine volume during the experiment. The data measured by the ultrasonic sensor are applied to train the Gaussian process. The mean absolute error (MAE) between the predicted urine volume and the actual urine volume as measured by the ultrasonic sensor were calculated to evaluate the accuracy of the prediction. The results showed that the average MAE is 71.08 ml.
{"title":"Prediction of Intravesical Urine Volume Considering Individual Characteristics on Accumulative Transition","authors":"T. Yamasaki, T. Kaburagi, Kaoru Kuramoto, S. Kumagai, T. Matsumoto, Y. Kurihara","doi":"10.33422/3rd.stkconf.2019.03.174","DOIUrl":"https://doi.org/10.33422/3rd.stkconf.2019.03.174","url":null,"abstract":"Urinary tract infections (UTI) are common infections in healthcare facilities and hospitals. Staff members often manage their patients’ urination needs with a catheterization, which relapses the UTI. Therefore, patients need another way for staff to take care of each patients’ urination without catheterization, though this increases the staff’s work burden. If staff could predict how the urine volume accumulates in the bladder in advance, a care schedule could be made easily; and can reduce staff’s work burden. Therefore, we propose a method for predicting the volume of accumulated urine utilizing data measured by an ultrasonic sensor. The proposed method is based on a deterministic model as a differential equation that represents the dynamics of the urine volume accumulation in the bladder, and utilizes the multi-task Gaussian process technique in the model. The Gaussian process predicts urinary volume at each moment by learning patients’ individual characteristics on accumulative transition from the training data on people are mixed. To evaluate the accuracy of the proposed method, we carried out a validity experiment with six subjects. In the experiment, we asked the subject to wear the ultrasonic sensor to measure urine volume during the experiment. The data measured by the ultrasonic sensor are applied to train the Gaussian process. The mean absolute error (MAE) between the predicted urine volume and the actual urine volume as measured by the ultrasonic sensor were calculated to evaluate the accuracy of the prediction. The results showed that the average MAE is 71.08 ml.","PeriodicalId":119572,"journal":{"name":"Proceedings of 3rd International Conference on Applied Research in Science, Technology and Knowledge","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124925975","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-03-15DOI: 10.33422/3rd.stkconf.2019.03.176
T. Yamasaki, T. Kaburagi, Kaoru Kuramoto, S. Kumagai, T. Matsumoto, Y. Kurihara
Activities of the working memory that site at several areas in the brain are essential for information transmission in language processing. Here, we assume that a network structure of information transmission corresponds to the language proficiency. In this paper, we propose a method to construct a language network by measuring the blood volume at the prefrontal lobe, one of the areas where the working memory is sited. Further, to quantify the language proficiency, we defined a score Sy that can be calculated from the language network. To validate the usefulness of the proposed method, we conducted an experiment with ten Japanese students who were asked to listen to the news in three languages: Japanese, English, and Vietnamese. Among the subjects, the proficiency in Japanese was the highest, English was intermediate, and Vietnamese was the lowest because all of them were native Japanese students; they had been learning English as a second language, and had never heard Vietnamese. Their respective blood volumes were measured by near-infrared spectroscopy (NIRS) while listening to the news. To obtain the network structure, the dynamic Bayesian network was applied to the data obtained by NIRS, and Sy was calculated for each language. The Sy scores for Japanese, English, and Vietnamese were −1430, −1443, and −1455, respectively. Therefore, the proposed method can represent the language proficiency because Sy for Japanese was the highest, English was intermediate, and Vietnamese was the lowest.
{"title":"Construction of a Language Network at Prefrontal Lobe while Listening and Evaluation of Language Proficiency","authors":"T. Yamasaki, T. Kaburagi, Kaoru Kuramoto, S. Kumagai, T. Matsumoto, Y. Kurihara","doi":"10.33422/3rd.stkconf.2019.03.176","DOIUrl":"https://doi.org/10.33422/3rd.stkconf.2019.03.176","url":null,"abstract":"Activities of the working memory that site at several areas in the brain are essential for information transmission in language processing. Here, we assume that a network structure of information transmission corresponds to the language proficiency. In this paper, we propose a method to construct a language network by measuring the blood volume at the prefrontal lobe, one of the areas where the working memory is sited. Further, to quantify the language proficiency, we defined a score Sy that can be calculated from the language network. To validate the usefulness of the proposed method, we conducted an experiment with ten Japanese students who were asked to listen to the news in three languages: Japanese, English, and Vietnamese. Among the subjects, the proficiency in Japanese was the highest, English was intermediate, and Vietnamese was the lowest because all of them were native Japanese students; they had been learning English as a second language, and had never heard Vietnamese. Their respective blood volumes were measured by near-infrared spectroscopy (NIRS) while listening to the news. To obtain the network structure, the dynamic Bayesian network was applied to the data obtained by NIRS, and Sy was calculated for each language. The Sy scores for Japanese, English, and Vietnamese were −1430, −1443, and −1455, respectively. Therefore, the proposed method can represent the language proficiency because Sy for Japanese was the highest, English was intermediate, and Vietnamese was the lowest.","PeriodicalId":119572,"journal":{"name":"Proceedings of 3rd International Conference on Applied Research in Science, Technology and Knowledge","volume":"53 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124529053","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-03-15DOI: 10.33422/3rd.stkconf.2019.03.173
K. Nishio, T. Kaburagi, S. Kumagai, T. Matsumoto, Y. Kurihara
{"title":"MATHEMATICAL MODEL FOR UNCONSTRAINED VIBRATION MEASUREMENT DEVICE TO ESTIMATE SCRATCHING TIME DURING SLEEP FOR DIAGNOSIS OF ATOPIC DERMATITIS","authors":"K. Nishio, T. Kaburagi, S. Kumagai, T. Matsumoto, Y. Kurihara","doi":"10.33422/3rd.stkconf.2019.03.173","DOIUrl":"https://doi.org/10.33422/3rd.stkconf.2019.03.173","url":null,"abstract":"","PeriodicalId":119572,"journal":{"name":"Proceedings of 3rd International Conference on Applied Research in Science, Technology and Knowledge","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115619176","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-03-15DOI: 10.33422/3rd.stkconf.2019.03.170
T. Yamasaki, T. Kaburagi, Kaoru Kuramoto, S. Kumagai, T. Matsumoto, Y. Kurihara
Increased number of falling in the bathroom are reported in ageing people due to the shock caused by temperature change, thus fall detection is an important area of study. We have proposed a fall detection method that can automatically detect the fall by calculating the frequency distribution trajectory from a signal measured by a microwave Doppler sensor in the living room. However, while applying the conventional method to the bathroom, wetness and shower-water noise can influence the fall detection. Hence, in this study, we propose a novel fall detection technique that can be robust against the presence of wetness and shower-water noise. In the proposed method, the frequency band of the noise due to wetness or shower-water is identified by an experiment; and the cut-off frequency of the high-pass filter is designed to remove the noise. Furthermore, we combine the dynamic time warping technique and the support vector machine to differentiate fall detection. To validate the usefulness of the proposed method, we conducted an experiment with a male subject. The environmental conditions are set as, dryness, filling the bathtub with hot water, filling the bathtub and start the shower. The subject was asked to perform the non-fall motions (picking-up, sitting-down, straddling the bathtub and washing the body, namely), and fall motions (falling due to straddling the bathtub, falling with slipping, falling due to fainting and falling by collapsing, namely). As a result, the accuracy to differentiate fall motions and non-fall motions is 0.95, whereas in conventional method, it was 0.81.
{"title":"Fall Detection Method using a Microwave Doppler Sensor in Bathroom Considering Effects of Wetness Condition","authors":"T. Yamasaki, T. Kaburagi, Kaoru Kuramoto, S. Kumagai, T. Matsumoto, Y. Kurihara","doi":"10.33422/3rd.stkconf.2019.03.170","DOIUrl":"https://doi.org/10.33422/3rd.stkconf.2019.03.170","url":null,"abstract":"Increased number of falling in the bathroom are reported in ageing people due to the shock caused by temperature change, thus fall detection is an important area of study. We have proposed a fall detection method that can automatically detect the fall by calculating the frequency distribution trajectory from a signal measured by a microwave Doppler sensor in the living room. However, while applying the conventional method to the bathroom, wetness and shower-water noise can influence the fall detection. Hence, in this study, we propose a novel fall detection technique that can be robust against the presence of wetness and shower-water noise. In the proposed method, the frequency band of the noise due to wetness or shower-water is identified by an experiment; and the cut-off frequency of the high-pass filter is designed to remove the noise. Furthermore, we combine the dynamic time warping technique and the support vector machine to differentiate fall detection. To validate the usefulness of the proposed method, we conducted an experiment with a male subject. The environmental conditions are set as, dryness, filling the bathtub with hot water, filling the bathtub and start the shower. The subject was asked to perform the non-fall motions (picking-up, sitting-down, straddling the bathtub and washing the body, namely), and fall motions (falling due to straddling the bathtub, falling with slipping, falling due to fainting and falling by collapsing, namely). As a result, the accuracy to differentiate fall motions and non-fall motions is 0.95, whereas in conventional method, it was 0.81.","PeriodicalId":119572,"journal":{"name":"Proceedings of 3rd International Conference on Applied Research in Science, Technology and Knowledge","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115826173","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-03-15DOI: 10.33422/3rd.stkconf.2019.03.178
T. Yamasaki, T. Kaburagi, Kaoru Kuramoto, S. Kumagai, T. Matsumoto, Y. Kurihara
E-learning has been adopted by several companies for qualification acquisition. In e-learning, multiple choice questions are often used, and learners are required to choose answers that seem correct to them. Reviews are indispensable to address inconsistencies and shortcomings in knowledge; therefore, questions that are incorrectly answered are mainly extracted for review. However, among correctly answered questions, there may be some that the learner chose without confidence; these questions should also be extracted for review. In this study, we propose a method that can estimate the confidence level when a learner chooses answers for multiple choice questions by applying penalized regression to the learner’s biosignals. In the proposed method, the following biosignals are measured from the learner for each problem: thinking time, operation time, transition distance of the gaze, maximum inclination angle and movement distance of the head, appearance rate of four brain waves, standard deviation of the RR interval, and root mean square of the difference between adjacent RR intervals. Feature values are calculated by the biosignals, and the confidence level is estimated by applying the feature values to the penalized regression. We conducted an experiment with six subjects to evaluate the method. They answered 29 multiple choice questions and a questionnaire about the confidence level for each answer. In the experiment, we compared the correct rate for estimation among ridge regression, lasso regression, and elastic net. Results show that the values of the average of correct rates are 56.90%, 67.24%, and 64.95% for ridge, lasso, and elastic net, respectively.
{"title":"Estimating Learner’s Confidence Level for Multiple choice Questions Using the Penalized Regression Method","authors":"T. Yamasaki, T. Kaburagi, Kaoru Kuramoto, S. Kumagai, T. Matsumoto, Y. Kurihara","doi":"10.33422/3rd.stkconf.2019.03.178","DOIUrl":"https://doi.org/10.33422/3rd.stkconf.2019.03.178","url":null,"abstract":"E-learning has been adopted by several companies for qualification acquisition. In e-learning, multiple choice questions are often used, and learners are required to choose answers that seem correct to them. Reviews are indispensable to address inconsistencies and shortcomings in knowledge; therefore, questions that are incorrectly answered are mainly extracted for review. However, among correctly answered questions, there may be some that the learner chose without confidence; these questions should also be extracted for review. In this study, we propose a method that can estimate the confidence level when a learner chooses answers for multiple choice questions by applying penalized regression to the learner’s biosignals. In the proposed method, the following biosignals are measured from the learner for each problem: thinking time, operation time, transition distance of the gaze, maximum inclination angle and movement distance of the head, appearance rate of four brain waves, standard deviation of the RR interval, and root mean square of the difference between adjacent RR intervals. Feature values are calculated by the biosignals, and the confidence level is estimated by applying the feature values to the penalized regression. We conducted an experiment with six subjects to evaluate the method. They answered 29 multiple choice questions and a questionnaire about the confidence level for each answer. In the experiment, we compared the correct rate for estimation among ridge regression, lasso regression, and elastic net. Results show that the values of the average of correct rates are 56.90%, 67.24%, and 64.95% for ridge, lasso, and elastic net, respectively.","PeriodicalId":119572,"journal":{"name":"Proceedings of 3rd International Conference on Applied Research in Science, Technology and Knowledge","volume":"92 24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128864705","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-03-15DOI: 10.33422/3rd.stkconf.2019.03.177
Htin Lin Oo
{"title":"Optimization of a parameter in laser heating process","authors":"Htin Lin Oo","doi":"10.33422/3rd.stkconf.2019.03.177","DOIUrl":"https://doi.org/10.33422/3rd.stkconf.2019.03.177","url":null,"abstract":"","PeriodicalId":119572,"journal":{"name":"Proceedings of 3rd International Conference on Applied Research in Science, Technology and Knowledge","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128038340","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-03-15DOI: 10.33422/3rd.stkconf.2019.03.171
Kaoru Kuramoto, Y. Kurihara, T. Matsumoto, S. Kumagai
{"title":"Analysis of sleep quality by bedroom environment data and daily activity data","authors":"Kaoru Kuramoto, Y. Kurihara, T. Matsumoto, S. Kumagai","doi":"10.33422/3rd.stkconf.2019.03.171","DOIUrl":"https://doi.org/10.33422/3rd.stkconf.2019.03.171","url":null,"abstract":"","PeriodicalId":119572,"journal":{"name":"Proceedings of 3rd International Conference on Applied Research in Science, Technology and Knowledge","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121698401","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-03-15DOI: 10.33422/3rd.stkconf.2019.03.169
Mehdi Mehrab-Kermani
A hybrid thermal management system (TMS) for high power lithium-ion battery modules of EVs is introduced with low energy consumption and reliability in a real state driving condition. An experimental investigation was performed to compare the hybrid TMS with an active air-cooling and a passive TMSs. We employed all three TMSs in standard weather condition of 24 °C. Although in the active TMS, the average temperature of the cell and module surface reached a steady state under safety temperature of 60 °C and 40 ºC respectively, the surface temperature non-uniformity was a chief problem. Consequently, the heat accumulation in PCMs caused by low thermal conductivity resulted in the failure of passive TMS. Our experiment reveals that while the airspeed (vehicle speed) was an only 3.2 km/h (2.0 mph), the hybrid TMS could entirely keep the module surface temperature under 40 °C. For dynamic mode, a study of driving cycle in comparison with US, Europe, and Japan driving cycle data was conducted to perform a dynamic model based on the Tehran traffic to challenge our TMSs in a real driving state including high and standard discharge rate and a stop mode in which there was no air convection. The results showed that just in the hybrid TMS, the cell surface could reach a steady state under 60 °C while the active TMS could keep temperature only for three cycles. Furthermore, our test proved that the proposed hybrid TMS maintains outstanding reliability and efficiency in the hot weather condition of 40 °C.
{"title":"An Innovative thermal management system for Lithium-ion battery under a real driving condition","authors":"Mehdi Mehrab-Kermani","doi":"10.33422/3rd.stkconf.2019.03.169","DOIUrl":"https://doi.org/10.33422/3rd.stkconf.2019.03.169","url":null,"abstract":"A hybrid thermal management system (TMS) for high power lithium-ion battery modules of EVs is introduced with low energy consumption and reliability in a real state driving condition. An experimental investigation was performed to compare the hybrid TMS with an active air-cooling and a passive TMSs. We employed all three TMSs in standard weather condition of 24 °C. Although in the active TMS, the average temperature of the cell and module surface reached a steady state under safety temperature of 60 °C and 40 ºC respectively, the surface temperature non-uniformity was a chief problem. Consequently, the heat accumulation in PCMs caused by low thermal conductivity resulted in the failure of passive TMS. Our experiment reveals that while the airspeed (vehicle speed) was an only 3.2 km/h (2.0 mph), the hybrid TMS could entirely keep the module surface temperature under 40 °C. For dynamic mode, a study of driving cycle in comparison with US, Europe, and Japan driving cycle data was conducted to perform a dynamic model based on the Tehran traffic to challenge our TMSs in a real driving state including high and standard discharge rate and a stop mode in which there was no air convection. The results showed that just in the hybrid TMS, the cell surface could reach a steady state under 60 °C while the active TMS could keep temperature only for three cycles. Furthermore, our test proved that the proposed hybrid TMS maintains outstanding reliability and efficiency in the hot weather condition of 40 °C.","PeriodicalId":119572,"journal":{"name":"Proceedings of 3rd International Conference on Applied Research in Science, Technology and Knowledge","volume":"4 8","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113962021","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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