The hypersonic target detection and recognition system is studied,on the basis of overall planning and design,a multi-agent system(MAS)structure and intelligent+information processing mechanism based on target detecti...The hypersonic target detection and recognition system is studied,on the basis of overall planning and design,a multi-agent system(MAS)structure and intelligent+information processing mechanism based on target detection and recognition are proposed,and the multi-agent operation process is analyzed and designed in detail.In the specific agents construction,the information fusion technology is introduced to defining the embedded agents and their interrelations in the system structure,and the intelligent processing ability of complex and uncertain problems is emphatically analyzed from the aspects of autonomy and collaboration.The aim is to optimize the information processing strategy of the hypersonic target detection and recognition system and improve the robustness and rapidity of the system.展开更多
Deep learning based recommendation methods, such as the recurrent neural network based recommendation method(RNNRec) and the gated recurrent unit(GRU) based recommendation method(GRURec), are proposed to solve the pro...Deep learning based recommendation methods, such as the recurrent neural network based recommendation method(RNNRec) and the gated recurrent unit(GRU) based recommendation method(GRURec), are proposed to solve the problem of time heterogeneous feedback recommendation. These methods out-perform several state-of-the-art methods. However, in RNNRec and GRURec, action vectors and item vectors are shared among users. The different meanings of the same action for different users are not considered. Similarly, different user preference for the same item is also ignored. To address this problem, the models of RNNRec and GRURec are modified in this paper. In the proposed methods, action vectors and item vectors are transformed into the user space for each user firstly, and then the transformed vectors are fed into the original neural networks of RNNRec and GRURec. The transformed action vectors and item vectors represent the user specified meaning of actions and the preference for items, which makes the proposed method obtain more accurate recommendation results. The experimental results on two real-life datasets indicate that the proposed method outperforms RNNRec and GRURec as well as other state-of-the-art approaches in most cases.展开更多
目的:丝氨酸蛋白酶抑制剂A3(SerpinA3)在急性肾损伤(AKI)中的诊断及其预后价值。方法:前瞻性纳入广东省人民医院重症监护病房(ICU)收治的AKI患者(AKI组)93例和同期ICU非AKI患者(对照组)89例,用ELISA法检测患者的血液和尿液SerpinA3。收...目的:丝氨酸蛋白酶抑制剂A3(SerpinA3)在急性肾损伤(AKI)中的诊断及其预后价值。方法:前瞻性纳入广东省人民医院重症监护病房(ICU)收治的AKI患者(AKI组)93例和同期ICU非AKI患者(对照组)89例,用ELISA法检测患者的血液和尿液SerpinA3。收集患者临床资料及实验室检测数据。采用Spearman法分析血SerpinA3、尿SerpinA3与临床指标的相关性。对AKI患者随访28 d,用受试者工作曲线(ROC)和生存曲线对患者28 d存活及死亡进行分析。结果:AKI患者血SerpinA3[158250(107025,259575)ng/mL vs 125850(80775,196575)ng/mL,P<0.05]和尿SerpinA3[1618(678.8,5496)ng/mL vs 345.0(173.8,675.0)ng/mL,P<0.05]均较非AKI组显著增高。Spearman分析显示,尿SerpinA3与血清肌酐呈正相关(r=0.213,P<0.05)。尿SerpinA3在预测AKI患者的28 dd内病死率ROC曲线下面积为0.8,敏感度和特异度分别为72.73%和77.24%。Kaplan-Meier生存曲线分析结果显示,发生AKI时尿SerpinA3>5496 ng/mL的患者,28 d内生存率显著降低,预后较差。结论:SerpinA3对AKI诊断和预测患者预后有一定的价值,可作为AKI患者诊断和预后判断的生物学标志物。展开更多
基金This work was supported by the National Natural Science Foundation of China(61471391).
文摘The hypersonic target detection and recognition system is studied,on the basis of overall planning and design,a multi-agent system(MAS)structure and intelligent+information processing mechanism based on target detection and recognition are proposed,and the multi-agent operation process is analyzed and designed in detail.In the specific agents construction,the information fusion technology is introduced to defining the embedded agents and their interrelations in the system structure,and the intelligent processing ability of complex and uncertain problems is emphatically analyzed from the aspects of autonomy and collaboration.The aim is to optimize the information processing strategy of the hypersonic target detection and recognition system and improve the robustness and rapidity of the system.
基金supported by the National Natural Science Foundation of China(61403350)。
文摘Deep learning based recommendation methods, such as the recurrent neural network based recommendation method(RNNRec) and the gated recurrent unit(GRU) based recommendation method(GRURec), are proposed to solve the problem of time heterogeneous feedback recommendation. These methods out-perform several state-of-the-art methods. However, in RNNRec and GRURec, action vectors and item vectors are shared among users. The different meanings of the same action for different users are not considered. Similarly, different user preference for the same item is also ignored. To address this problem, the models of RNNRec and GRURec are modified in this paper. In the proposed methods, action vectors and item vectors are transformed into the user space for each user firstly, and then the transformed vectors are fed into the original neural networks of RNNRec and GRURec. The transformed action vectors and item vectors represent the user specified meaning of actions and the preference for items, which makes the proposed method obtain more accurate recommendation results. The experimental results on two real-life datasets indicate that the proposed method outperforms RNNRec and GRURec as well as other state-of-the-art approaches in most cases.
文摘目的:丝氨酸蛋白酶抑制剂A3(SerpinA3)在急性肾损伤(AKI)中的诊断及其预后价值。方法:前瞻性纳入广东省人民医院重症监护病房(ICU)收治的AKI患者(AKI组)93例和同期ICU非AKI患者(对照组)89例,用ELISA法检测患者的血液和尿液SerpinA3。收集患者临床资料及实验室检测数据。采用Spearman法分析血SerpinA3、尿SerpinA3与临床指标的相关性。对AKI患者随访28 d,用受试者工作曲线(ROC)和生存曲线对患者28 d存活及死亡进行分析。结果:AKI患者血SerpinA3[158250(107025,259575)ng/mL vs 125850(80775,196575)ng/mL,P<0.05]和尿SerpinA3[1618(678.8,5496)ng/mL vs 345.0(173.8,675.0)ng/mL,P<0.05]均较非AKI组显著增高。Spearman分析显示,尿SerpinA3与血清肌酐呈正相关(r=0.213,P<0.05)。尿SerpinA3在预测AKI患者的28 dd内病死率ROC曲线下面积为0.8,敏感度和特异度分别为72.73%和77.24%。Kaplan-Meier生存曲线分析结果显示,发生AKI时尿SerpinA3>5496 ng/mL的患者,28 d内生存率显著降低,预后较差。结论:SerpinA3对AKI诊断和预测患者预后有一定的价值,可作为AKI患者诊断和预后判断的生物学标志物。
