WSNs Based- L4L Rule for Fuzzy Inventory Control Decision Making (WSN-FL4L)

تعد وظيفة السيطرة على الخزين واحدة من اهم الانشطة التي تقوم بها الشركات الصناعية . تلعب انظمة السيطرة على الخزين دور كبير في ضمان الرقابة التامة على عناصر المخزون. من جهة ثانية،  نال استخدام تقنية شبكات الاستشعار اللاسلكية اهتماماً كبيراً من حيث تطبيقها في حقل السيطرة على الخزين كونها تقنية كفوءة و رخيصة الثمن و سهلة التنصيب . اقترح البحث الحالي نظام سيطرة مخزنية ذكي بالاعتماد على تقنية الاستشعار اللاسلكي و المنطق المضبب، بالاعتماد على قاعدة حجم الدفعة المكافيء للاحتياج  ضمن نظام تخطيط الاحتياجات المادية ليمكن استخدامه لحل اغلب المشاكل الصناعية. تم تطبيق النظام المقترح على حالة افتراضية لمنتوج يمثل قطعة من الاثاث الخشبي خماسي الدواليب الجرارة.  تكمن فائدة النظام المقترح في قدرته على المساعدة في اتخاذ قرارات المخزون في ظل حالات مختلفة فقد تم اختبار حالات متعددة لاعادة تعزيز المخزون بالاعتماد على قاعدة (اذا – عند ذلك) من المنطق الضبابي. تم نشر وبرمجة حساسات لقياس المسافة لغرض حساب عدد قطع مفردات المخزون بالاعتماد على قياس سمك القطعة.. تم اعتماد تقنية ASP.net و لغة C# بالاعتماد على قاعدة بيانات في الخادم الرئيس. Inventory control is one of the most important tasks for industrial companies. Inventory control systems play an important role in inventory monitoring. Wireless Sensor Networks (WSNs) technology has been taken into great attention in the field of inventory control because it is considered as an efficient, low-cost technology to control inventories. This paper proposes an intelligent inventory control system depending on WSNs and fuzzy logic to control the dependent demand items effectively under MRP system and L4L lot sizing rule. to be used to solve the industrial problems. A numerical example of a 5 drawers - chest of drawers product has been experimented. The advantage of the proposed WSN-FL4L system could be represented in its ability to make decisions in different inventory states. Different cases of replenishment were investigated depending on the (IF-THEN rules) statement of fuzzy logic. Ultrasonic - distance sensor has been programmed to compute the number of items’ pieces, depending on the thickness of each piece. ASP.net web application and C# programming language based on SQL database server are applied

Crowd counting using Yolov5 and KCF

Crowd detection has various applications nowadays. However, detecting humans in crowded circumstances is difficult because the features of different objects conflict, making cross-state detection impossible. Detectors in the overlapping zone may therefore overreact. In this paper, real-time people counting is proposed using a proposed model of the YOLOv5 (You Only Look Once) algorithm and KCF (kernel correlation filter) algorithm. The YOLOv5 algorithm was used because it is considered one of the most accurate algorithms for detecting people in real time. Despite the high accuracy of the YOLOv5 algorithm in detecting the people in the image, video or real-time camera capturing, it needs an increase in speed. For this reason, the YOLOv5 algorithm was combined with the KCF tracking algorithm. Where the YOLOv5 algorithm identifies people to be tracked by the KCF. The YOLOv5 algorithm was trained on a database of people, and the system's accuracy reached 98%. The speed of the proposed system was increased after adding the KCF

Comparison YOLOv5 Family for Human Crowd Detection

Recent years have seen widespread application of crowd counting and detection technology in areas as varied as urban preventing crime, station crowd statistics, and people flow studies. However, getting accurate placements and improving audience counting performance in dense scenes still has challenges, and it pays to devote a lot of effort to it. In this paper, crowd counting models are proposed based on the YOLOv5 algorithm, and four YOLOv5 models (YOLOv5l, YOLOv5m, YOLOv5s, YOLOv5x) were built for the purpose of comparing the models and increasing the accuracy of crowd identification as each model contains certain characteristics such as Filter sizes. Each model was trained on a human dataset (indoor and outdoor) for the purpose of comparing the results of each model and showing which model reaches higher accuracy in detecting people. Through this study and practical experiments conducted on each model, it was found that the best model is YOLOv5x, and YOLOv5l, where the accuracy of detecting humans reached more than 96%, while YOLOv5s reached more than 92%, and YOLOv5m reached the lowest accuracy, which is 91%